Continuous Storage Root Formation and Bulking in Sweetpotato.

This study investigated the phenotypic variation of continuous storage root formation and bulking (CSRFAB) growth patterns underlying the development of sweetpotato genotypes for identification of potential varieties adapted to piecemeal harvesting for small scale farmers. The research was conducted between September 2016 and August 2017 in Uganda. Genotypes from two distinct sweetpotato genepool populations (Population Uganda A and Population Uganda B) comprising 130 genotypes, previously separated using 31 simple sequence repeat (SSR) markers were used. Measurements (4 harvest times with 4 plants each) were repeated on genotypes in a randomized complete block design with 2 replications in 2 locations for 2 seasons. We developed a scoring scale of 1 to 9 and used it to compare growth changes between consecutive harvests. Data analysis was done using residual or restricted maximum likelihood (REML) in GenStat 18th Edition. There were strong linear and quadratic trends over time (P<0.001) indicating a non-linear growth pattern within and between locations, seasons, and genotypes for most traits. Some genotypes displayed early initiation and a determinate linear increase of bulking, while others showed late initiation following a quadratic growth pattern. Broad sense heritability of CSRFAB would be low due to large GxE interactions, however, it was relatively high (50.5%) compared to other yield related traits indicating high genetic influence and accuracy of the developed method to quantify yield overtime. A high level of reproducibility (89%) was observed comparing 2016B and 2017A seasons (A and B are first and second season, respectively) at the National Crops Resources 1 1 1


Amendments from Version 2
We thank the reviewer for critically reviewing our manuscript and for providing very useful suggestions for improving the quality of the manuscript to reach a broad audience. We are in agreement with his suggestions. We have to the extent possible revised the manuscript as recommended and a detailed report of responses is provided. The main differences between this version and the previous version are: We have removed detailed explanations on statistics and technical details under the Methods and analysis results sections and under the Results and discussion section while maintaining content for good flow to enable the broad audience to follow the article content.
The text under the Experimental design sub-title has been revised to improve flow.
The explanation due to increase in area cultivated and research effort for the increased production of sweetpotato in the East African countries has been included in the introduction section.
The term, mega-environment, has been replaced with an equivalent of environmental diversity.
In the Results and discussion section, long explanations of mainly statistical details without major contribution to the understanding of continuous storage formation and bulking and its implication in improvement of sweetpotato yield gain have been eliminated to improve clarity and address the concerns raised by Dr. Alfonso del Rio under "additional comments".
A statement on competing interests has been included in the manuscript.
We hope that the modifications and shortening of the text to improve flow and clarity have improved the quality of the manuscript. However, further suggestions the reviewer(s) might have are still welcome.

REVISED
an area of about 8 million ha and an average global yield of 11.1 tons/ha (FAO, 2016). Asia is the world's largest sweetpotato producing continent, with 79 million tons, followed by Africa (FAOstat, 2016). About 75% of this global production is from China alone. A total of 21.3 million tons is produced in Africa, with 48% from the Great Lakes region. In East Africa, the crop is the second most important root crop after cassava and has played an important role as a famine-relief crop during its long history and has recently been reevaluated as a health-promoting food (Low et al., 2017). Uganda ranks as the fourth largest sweetpotato producer in the world after China, Nigeria and Tanzania, with a production of 2.1 million t. In Africa, Uganda is ranked third after Nigeria and Tanzania. Sweetpotato is one of the main staple crops in the food systems of Uganda, Rwanda, and Burundi with a per capita consumption of 50.9, 80.1 and 57.0 kg, respectively (Table 1).
On average and across the 30-year period, the population across East Africa increased by two-and-half-fold while sweetpotato production increased by two-fold. The increase in production was due to a combination of factors varying in different countries but mainly due to increase in area and breeding efforts. Statistics in general underestimate production in most annual plants since not all crop production is recorded. Usually, only the main planting season is recorded even though crops are grown over two to three growing seasons per year (Bararyenya et al., 2018a). The most noticeable production increase took place in Tanzania with more than ten-fold increase of tonnage following an almost two-fold increase in growing area and almost four-fold increase in yields. The highest productivity is recorded in Kenya, with more than 17 t/ha/yr, followed by Tanzania (12.5 t/ha/yr). In other East African countries, productivity has more or less stagnated. The yield increase in the region over the 30-year period can be attributed to breeding and release of improved varieties by national breeding programs, and to the introduction of new varieties mainly, by the International Potato Center (CIP). The increase in yield can also be explained by the importance and recent interest in sweetpotato as a food and nutritious crop compared to the early 1990s when it was hardly known.  (Table 1) compared to the achievable yields of over 40 t/ha under improved conditions. The low yield could be attributed partly to the growing of low yielding and highly disease susceptible landraces by small-scale farmers. Abidin (2004) reported high farmer preference for their landraces, while Sseruwu (2012) associated this preference with lack of farmer desired attributes in the newly released varieties. In Uganda and other East African countries, piecemeal harvesting, characterized by repeated harvesting from the same sweetpotato plants on a mound, ridge or other seedbed over time, is the predominant mode of harvesting among subsistence and commercial sweetpotato farmers. Importantly, this practice is also known in other root and tuber crops including potato, cassava and yam. Recently, Tadesse et al. (2017) reported that piecemeal harvesting was the dominant practice among poor potato farmers (60% of respondents) in Ethiopia, whereas the majority of the wealthy and medium-wealthy farmers combined piecemeal harvesting with harvesting all at once. Farmers harvest enough for one or few meal(s), or enough for sale. This is because these crops are very difficult to store, and storage for fresh sweetpotato produce is virtually non-existent. Therefore, the practice allows for in-ground storability and market partitioning during the cropping season. It also creates room for new storage roots to initiate and enlarge for the next harvest. However, breeding and selection have been based on a single harvest and no breeding research has tried to understand the genetics underlying traits associated with these common practices in sweetpotato farming systems. CSRFAB in sweetpotato is associated with the perennial nature of the crop and allows for longer photosynthetic activity resulting from persistent canopy development leading to increased plant productivity. This is supported in the case of practices in Uganda by the fact that more than 90% (n = 350) of the farmers have no knowledge of the maturity periods of the local sweetpotato varieties they grow (Bashaasha et al., 1995).
Maturity periods in sweetpotato vary with genotype and the environmental conditions under which they are grown. Knowledge of the growth patterns of storage root formation and bulking traits is critical for crop yield improvement, crop management, and specifically for timing fertilizer application and irrigation. Sweetpotato has been for centuries selected for its starchy roots on seasonal basis and may have been progressively losing its perennial feature. It is well known in other root and tuber crops that yields are a result of the rate and duration of the period of tuberization, which in turn depend on longevity of the leaves, the beginning of storage root formation, and duration of the growth cycle. In almost all experiments, the end time of storage root formation was not defined or properly assessed; number of storage roots was infrequently recorded and maximum number of storage roots was never established. The effect of longer vegetative maintenance periods of green leaves observed in some genotypes has never been investigated in sweetpotato, but it is reported to influence greater productivity in potato (Çalişkan et al., 2007). Despite these deficiencies, the storage root formation and bulking patterns are widely regarded as a key developmental stage in the crop's life, having profound implications for subsequent growth and development.
It was therefore hypothesized that longevity of green foliage due to genetic properties in CSRFAB sweetpotato genotypes is greater than in discontinuous storage formation and bulking (DSRFAB) genotypes. Consequently, the extended period of green leaves for CSRFAB genotypes will impact storage root formation and bulking leading to a significant increase in yield. This expected variation in yield and yield components is mainly due to longer duration of photosynthetic activity and the great availability of photo-synthesizing material, mostly its green leaves (Paltridge et al., 1984). Thus, the amount of change in the mean value of expected responses associated with a unit increase in growth time, holding all other variables constant, varies with increased growth time periods of some sweetpotato cultivars and produces higher amounts of storage root number and weight. To really understand the evolution of a trait, we need to know whether any variability in that trait can be assigned to genetic effects. If so, and if there is fitness variation associated with the trait; it will be subject to natural selection (Croston et al., 2015). This study investigated genetic variability of CSRFAB and, characterized growth patterns at different development stages to identify possible CSRFAB sweetpotato genotypes in the germplasm collection in Uganda for use as parents in improvement of the trait.

Development of a scale for continuous storage root formation and bulking (CSRFAB).
To capture the possible overtime initiation and bulking in sweetpotato, we developed a rating scale of 1 to 9. The scale rates the expression of CSRFAB trait for an individual genotype. CSRFAB scores were therefore developed throughout the 2016B growing season. Detailed observation of storage root formation and bulking over 6 months provided comprehensive knowledge of the development of a scale for measuring changes in the CSRFAB trait. The scale was set to measure the changes in root formation and bulking of a given individual genotype. The change in scores would reflect the potential of roots to bulk into storage roots (fleshy or lignified), newly initiated storage roots, their status of bulking and their maturity levels (see Figure 1, scoring scale).

Data analysis
Growth model analysis. Growth models were estimated using a multilevel, mixed model framework described by Payne (2009) in GenStat 18 th Edition. Each of the 130 genotypes was maximally compared (4160 times) with the other genotypes in a model with two replications, four harvest times, two seasons and two locations.
To better understand the details in the storage root development patterns across the four harvest times, we analyzed piecewise, storage root growth patterns in different phases of the trial and selected individual plants to represent different growth pattern characteristics. Means were plotted to visualize the growth patterns using Genstat 18 th edition.
Restricted maximum likelihood (REML) variance components analysis of the phenotypic data was performed using the following general linear mixed model; µ, is the overall mean, S i is the effect of the ith season, L j is the effect of the jth location, SLR ijk is the effect of the interaction between the ith season in the jth location and the kth replication,  HT is the random effect of the cubic term in the polynomial model, SH il is the interaction between season and harvest time, LH il is the random effect of interaction between jth location and h th harvest time, SLH iil is the effect of the interaction between the ith season and the jth location at the mth harvest time, E m is the effect of the m th genotype, 1 lm EH is the effect of interaction between mth genotype and lth linear term, 2 qm EH is the effect of the interaction between ith genotype and qth quadratic term in the polynomial model, 3 cm EH is the effect of interaction between ith genotype and cth cubic term in the polynomial model, SE im is the effect of interaction between season and genotype, LE jm is the effect of interaction between location and genotype, SLE ijm is the random effects of the interaction between season, location and genotype. €SEH iml , is the effect of the interaction between season, harvest time and genotypes, LEH jml is the random effect of the interaction between location, harvest time and harvest time, SLEH ijml is the interaction season, location and harvest time, Ɛ ijklcqm is the error associated with all factors involved in the polynomial model.
The F-test denominator for genotypes tested in different locations and seasons (Bararyenya et al., 2018b) was calculated following Satterthwaite's formula for the "composite" F-test denominator for genotypes:

Results and discussion
Growth pattern analysis and phenotypic variability across selected CSRFAB sweetpotato traits.
We used a linear mixed model to decompose phenotypic variance (P) into different components: genetic (G) and environmental (E) sources, and their interaction effects (GxE). For most of the traits, their three-way interactions were significant which means in reality that at least one of the 2-way interactions changes across the third factor. Thus, the interaction between season, location and replication (SLR) was highly significant across all the parameters. This is because the replication differences were large in some environments as previously observed   by Tumwegamire et al. (2016). The variability in replication for an experiment involving clonal crops can easily occur. The main effects for season and location were not significant, as were their interactions. The interaction between season, location and harvest time (SLH) was highly significant (P<0.001) for all the parameters in this study, resulting in non-significance of SL, SH and LH interactions. This implies that harvest time effect differs depending on the level of the location and season on storage root yield (SRY), CSRFAB, storage root diameter (SRD), storage root length (SRL), CRW, CRN, HI and SEN (Table 4). These results require further investigation to identify the level of influence (Table 4). For all the traits in this study, the interaction between season, location and genotype (SLE) was highly significant (P<0.001). This indicates that there was wide variability of genotype across locations and seasons and this wide variability can be used for sweetpotato yield improvement in a specific location. However, there is a need to study the stability across locations and seasons for easy selection of the CSRFAB trait.

Table 4. Mean squares and F-test of significance for continuous storage root formation and bulking (CSRFAB), storage root yield (SRY) (tons/ha), storage root number (SRNO), vine yield (VY) (tons/ha), storage root diameter (SRD) (mm), storage root length (SRL) (mm), commercial root number (CRN) and weight (CRW), harvest index (HI) and senescence (SEN) of
The derived overall means (Table 5) suggest the existence existence of determinate and extended growth maturity stages among genotypes. High variation (P<0.001) of storage root formation (root initiation) and bulking was observed using the 1 to 9 scoring scale for CSRFAB. This suggests that the scale can be used to differentiate and evaluate the CSRFAB trait among sweetpotato genotypes.
Variance component analysis and heritability estimates of selected sweetpotato growth traits Variance component analysis and heritability estimates of selected sweetpotato growth traits in a nonlinear model structure. The genotypic variance among the nine traits associated with CSRFAB varied from 0.9 (CRW) to 7.7 (SRNO). Genotypic variance was high for SRNO (7.7), CRN (5.0) and CRW (5.0) ( Table 5). The phenotypic variance was high for VY (449.8), SRL (378.5) and SRY (84.6) and varied from 2.4 (CSRFAB) to 449.8 (VY). The residual variance was extremely high for VY (412.5) and SRL (339.6). These two particular traits are influenced by continuous growth. While some genotypes continue to increase in biomass, others die by senescence, likewise for vine length. The error and the GxE variances for CSRFAB are not big compared to its genetic variance. This implies that the scale used to measure the trait is precise and the trait is not influenced much by the environment. Heritability was relatively high for SRNO (67.2%) and CSRFAB (50.5%). The low residual variance and high heritability observed for CSRFAB imply that the CSRFAB trait in this study was not greatly influenced by environment and the scale used to measure the trait is precise.

Genetic effects on yield of CSRFAB genotypes in sweetpotato
We performed a harvest basis analysis and calculated the corresponding heritability for each trait to study the dynamics of breeding values across harvest times. We focused on the traits that can affect the final yield and we compared the dynamic genetic variances and heritability of traits. Thus, the genotypic variances among seven main traits associated with CSRFAB varied from 0 to 72.46 (Table 6). Genotypic variance was high for HI at 120 DAP (72.46) and VY at 180 DAP (59.91) ( Table 6). Zero variance was recorded for VY (120 DAP), HI (150 DAP), SEN (180 DAP) and weevils (180 DAP). The zero (or negative) variance components could be due to an artifact of the optimization algorithm that includes a non-negative constraint; a negative variance component could also represent competition effects between adjacent plots in the same block in the field. These results imply that there is no significant change in VY, HI, SEN and weevil damage within the population for the respective growth periods. Heritability is the proportion of variance in a phenotypic trait that is accounted for by genetic variance. Therefore, these results affected the value of heritability in the respective traits at the same harvest periods (varied from 0 to 67%). The 0% heritability implies that the effect of the traits is moving to fixation, that is, its frequency in the population is close to 100%. If we score the effect of the allele (s) regulating the trait having no heritability in the population, the result would mean in genetic terms, that the allele frequency in the population is 100%, therefore the genetic variance at the loci of these genes is zero, so any variance in the corresponding phenotypic traits cannot be attributed to the non-existent genetic variance. These results need further investigation especially in the case of weevil resistance mechanisms as no significant increase in weevil infestation was observed in late harvest (Bararyenya et al., 2018b). The rate in storage root bulking resulting in the available high green biomass may compete with weevil infestation. Broad sense heritability was relatively high for CSFRAB (50.5%) compared to other yield component traits indicating a better yield prediction using the scale.
We found temporal dynamics of genotypic influence on overall trait development (Table 6). In the early growth phase, genotypic variance was mostly low. As plants grew, genotypic factors became in general more important. The increasing genetic effect was observed up to about 120 DAP and decreased thereafter. After 120 DAP, the genetic effect became relatively less important. This can be partly explained by the drought stress which became more important 4 months after planting (MAP). Although less obvious, the opposite pattern was seen in the growth recovery phase (after 5 MAP), likely due to growth resumption resulting in the decline in overall phenotypic differences between CSRFAB and DCSRFAB plants.
The investigated highly correlated traits with CSRFAB showed dynamic changes in heritability during the entire plant growth stage (Table 6). SRNO and CSRFAB showed similar patterns of heritability over time. We found that heritability of SRNO and CSRFAB increased in early growth stages and then decreased during drought stress, occurring between two consecutive rainy seasons and then increased thereafter during the growth recovery period occurring with the onset of the rainy season. These results are supported by Tuberosa (2012); quantitative traits reflecting the performance of crops under drought conditions tend to have low to modest heritability ( Table 6).

Identification of storage formation and bulking growth patterns and their characterization
We have proposed in this study a method that can be used to estimate changes (Figure 2 to Figure 5) in the CSRFAB trait and therefore estimate the potential maximum yield of a sweetpotato genotype. We characterized in this study growth patterns associated   with CSRFAB in sweetpotato and compared the accuracy of CSRFAB scores and the classic method used to measure growth change overtime (compared yield change overtime). The scoring method developed in this study accurately measured changes in the CSRFAB trait. This is supported by its low residual variance (1.4) compared to SRN (16.3) and SRY (51.1) ( Table 4) and its high broad sense heritability of 50.5% compared with heritability of SRN (67.2%) and yield (11.1%) ( Table 5). Two types of growth patterns were identified according to the lifetime of sweetpotato genotypes. The first type which has determinate storage root formation and bulking is characterized by a rapid and short vegetative growth period, followed by a senescence period until the leaves die off. This period is also characterized by a quick maximum yield obtained about 90 to 120 DAP ( Figure 2) for most DCSRFAB genotypes. Respective CSRFAB scores for the same genotypes declined overtime due to lack of new root formation and similar level of bulking for mature storage roots (Figure 3). Changes between successive harvest times were generally negative for DSRFAB genotypes. The cumulative yield changes, as were the cumulative score changes of DCSRFAB genotypes were therefore lower than the respective maximum single harvest yields and the rate of CSRFAB decreases in late stages. The second type of growth is characterized by a prolonged vegetative growth and a late switch-over time to reproductive phase (Figure 3 and Figure 5). The cumulative yield changes over time, as were the cumulative score changes, of CSRFAB genotypes displayed similar results with the last harvest (180 DAP) confirming the accuracy of the scale to predict yield changes over time. The change responses were positive for CSR-FAB genotypes and the cumulative responses increased overtime ( Figure 4) for yield and the increase was not significant ( Figure 5) for the CSRFAB scores suggesting a possibility of predicting CSRFAB genotypes responses from early growth stages. During this period the yield increase starts later and increases drastically after 150 DAP ( Figure 4) for most CSRFAB genotypes. Respective CSRFAB scores of the same genotypes did not change overtime due to continuous root formation and bulking ( Figure 5) suggesting the ability of the scoring scale to predict CSRFAB genotypes at early growth stages. This period increased drastically yields of CSRFAB genotypes, however, the maximum yields were not observed in this trial due to continued vegetative growth. According to Paltridge & Denholm (1974) the variation in yield observed may be due to the variation in their actual rates of dry matter production and the individual switchover time of the genotype to raise its maximum yield, referred to as the optimum growth pattern. The optimum growth pattern for determinate sweetpotato genotypes is of a two-phase plant growth, with the switchover occurring at the instant the plant becomes limited by restriction of its access to light (senescence). Subsequent values of the growing cycle which give maximum yield per unit time of harvest occur after these periods for indeterminate growth which are functions of above ground vegetative (leaves) lifetime. Each successive maximum is larger than the last, so one might expect the plants to evolve longer lifetimes and correspondingly longer periods of indeterminate (CSRFAB) growth. The intensity of senescence strongly varied (P<0.001) among genotypes and was negatively correlated with CSRFAB ( Table 7). The yield rate of change between the 120 DAP and the 150 DAP was always negative. . The rapid growth in the first stage for determinate or DSRFAB genotypes could be attributed to early maturing genotype properties in which there is more energy invested in biomass production for early remobilization for storage root bulking and this plant growth expression is also reported in other annual crops (Wenk & Falster, 2015). However, the maximum yield in this period was low and was limited by the available amount of green biomass which affected the bulking rate. Paltridge et al. (1984) reported similar common characteristics in which many plants have a rather sharp transition between the vegetative and the reproductive stages of growth. If the time of switch over is small, the amount of green leaf would be small and the subsequent rate of production and final yield would be also low. In this phase, the time of switchover was before 90 DAP. Sweetpotato continues to grow and branch if environmental conditions are favorable, due to its perennial habit, but the leaves formed earlier in the growing season start to fall and the total number of leaves and leaf area decrease toward the end of the growing season (Somda & Kays, 1990). Determinate or DSRFAB genotypes lost their ability to initiate new shoots at around 120 DAP and the VW decreased drastically. These results agree with findings of Paltridge & Denholm (1974) and Bhagsari (1990) in which most annuals exhibit a single reproductive phase, often with a sudden onset.
The senescence phenotype was less observed in indeterminate types, although greenness intensity was reduced at 120 DAP which resumed and drastically increased (high positive quadratic slope) from 150 DAP when rains came back. Bhagsari (1990) reported similar results that sweetpotato cultivars maintained leaf area index to intercept a major portion of sunlight until harvest, and leaf area growth significantly differed depending on cultivar.
The slow increase in most of the yield component parameters in CSRFAB genotypes resulted in low storage root yield in the first growth phases (Figure 3 & Figure 5). This can be explained by the fact that indeterminate genotypes invest resources for maintenance at earlier stages including root elongation for nutrition purposes and biomass production to maintain vegetative   growth. This resulted in strong vegetative quadratic biomass increase (Table 7) for CSRFAB genotypes. For these genotypes the time of switchover to reproduction was very late (around 150 DAP). This was the last growth phase where yield increased drastically in CSRFAB corresponding to the switch to reproduction (onset of flower and seed) and storage root remobilization in sweetpotato in which the plant invests its vegetative resources in increasing the sink capacity. Because of high availability of resource/energy in upper biomass, sink strength is increased leading to increased productivity.
For sweetpotato breeders and practitioners, harvest index (HI) is difficult to estimate due to the problem of measuring its components. For instance, the time of harvest influences greatly the value of HI because storage root bulking is likely to vary progressively from the onset of the storage roots and increases with the biomass translocation into storage roots. However, the senescence reduces the above-ground biomass progressively leading to high and unrealistic values (e.g. HI of 99.31%) ( Table 7). Therefore, HI of sweetpotato varies greatly with the time of harvest. This variation is also influenced by other environmental factors including wet or dry conditions during harvest which increase or decrease weight due high or less moisture. Similar results in other root and tuber crops are reviewed by Hay (1995).
Overall, the starting yield was low at 90 DAP (overall mean) and increased progressively as growth time increased (Table 7 and Table 8). This relationship is well explained by the variation in the data (R 2 varied from 38% to 65%). High yielding genotypes showed consistently maximum yields in the population (R 2 >0.99) while low yielding genotypes showed inconsistent relationship (low R 2 ). The maximum yield recorded across locations and seasons was 16.7 t/ha and 28.6 t/ha at the first harvest and the last harvest, respectively. This implies that the frequency of genotypes that increase yield overtime is high. In this trial, some genotypes reached maximum growth earlier, others late, leading to high variability in growth related parameters. These contrasting genotypes for CSRFAB can be used in breeding programs to study and improve sweetpotato yield in CSRFAB genotypes. The average yield at two locations in two seasons shows that you can more than double sweetpotato production by choosing high yielding genotypes ( Variability and distribution of 130 genotypes over 4 harvest times For SRY and VY the first two box plots are comparatively equally shorter than the third and fourth box plots ( Figure 6). This suggests that, overall, genotypes have little variation over the first two harvest times. It also suggests low variation between time of harvest during this phase. The third and fourth box plots are higher than the first two box plots for SRY and VY. This suggests that genotypes have quite different growth patterns from the two first harvest times and the two last ones. It also suggests high variation among the genotypes. The last two box plots in each sub-figure show obvious differences within each box plot and the two first box plots. This suggests an area of difference that could be explored further. The four sections of the box plots are uneven in size. This shows that many genotypes have a similar growth pattern during the early growth phase, but in a later phase genotypes are more variable in their growth pattern. The long upper whisker shows that genotype growth varied amongst the most positive quartile group, and very similar for the least positive quartile group. This suggests a need for further exploration. The medians of the two first harvest time plots (which generally will be close to the average) are at the same level. However, the box plot of the last two harvest times shows very different distribution means. Variation and distribution of CSRFAB scores across location and seasons consistently remained almost the same, suggesting an accurate response prediction of the scores from the beginning.
Relationship between associated growth traits and CSRFAB in sweetpotato CSRFAB scores were highly and positively correlated with most of the yield component traits (Table 9). This implies that CSRFAB is also a component of yield. In other words, the higher the CSRFAB scores of a sweetpotato genotype, the higher the yield of the genotype. CSRFAB scores were negatively correlated with SEN which confirms our hypothesis in which a CSRFAB genotype should maintain vegetative growth to continuously provide source/inputs for sink storage roots. The negative correlation with VW needs further investigation, however, the competition of source and sink activities in the plant, effects of pests and diseases such as nematodes and Alternaria stem blight (Alternaria bataticola) and SPVD could be among the causal factors. It is possible to cease vegetative growth and continue survival for an extended period, but most varieties reduce their vegetative weight following the storage root bulking peak (Venus & Causton, 1979). The comparison of a classic method of evaluating growth change (analyzing yield mean change between two consecutive harvests) and the developed scale for measuring CSRFAB traits produced similar results, however, CSRFAB was more accurate. This is supported by high heritability and low residual variance observed for CSRFAB versus yield and other component parameters. Using the developed scale, 48 genotypes were clustered among CSRFAB genotypes, whereas 62 genotypes clustered among DSRFAB ( Figure 5). These results were reproduced in the following season, and 41% were common to the two methods in 2017B versus 46% in 2017B (Figure 8).

Accuracy of CSRFAB scoring method
It was hypothesized that CSRFAB genotypes potentially increase yield over time, therefore, the trait can be screened for by measuring yield change overtime. This method, holding other factors constant, shall produce the same results of scoring for the trait using the developed 1 to 9 scale. We investigated this relationship in our data. Overall, the yield change analysis over four harvest times grouped the 110 genotypes (without missing data) into 39 CSRFAB and 71 DSRFAB genotypes. Using the developed scale, 48 genotypes clustered among the CSRFAB genotypes, whereas 62 genotypes clustered together as DSRFAB (Figure 8).
CSRFAB scores represent a measure of changes in CSRFAB on a scale of 1 to 9. Figure 9 shows the total number of clones that exhibited CSRFAB and DSRFAB in 2017A, the respective proportion of each category in the total population, the respective total number of clones that are common to a different method of screening and their respective proportions under each method of screening. The overall picture shows similarities, although the accuracy differs between methods. Comparing the score of 2017A and 2017B, there is high reproducibility of the results. This implies that the CSRFAB scale of 1 to 9 can be used accurately to estimate changes in CSRFAB genotypes and yield progress in sweetpotato crops overtime.
HI captures the allocation of biological yield into above-ground biomass and root biomass (with commercial and non-commercial storage and non-storage roots). Estimating the amount of non-storage and non-commercial roots is extremely difficult (Grüneberg  et al., 2015) because of newly initiated and immature storage roots that will affect the final yield of indeterminate genotypes. Usually, HI is calculated by storage root yield divided by above-ground biomass and storage root production which overestimates HI values. HI values are likely to increase with time of harvest for CSRFAB genotypes because the number and weight of storage roots increase overtime. CSRFAB, newly investigated in this research, highlights the need to integrate these complex yield components by estimating their effects leading to the final maximum yield, and introgressing genes controlling the trait into genotypes with other major desirable traits such as SPVD resistance, weevil resistance and drought tolerance to unleash the potential of sweetpotato.

Conclusion
This study highlights three important results: 1) the CSRFAB trait can be exploited to provide additional yield in sweetpotato 2) the 1 to 9 scale developed provided consistent scores across replications, and reflected well the growth patterns observable phenotypically, 3) the method of analyzing growth variables over harvest times revealed distinct growth patterns among genotypes. These patterns identify which sweetpotato genotypes are likely to be suited to piecemeal harvesting. The methodology introduced here is expected to be useful in other root crops as well.
This study showed that CSRFAB genotypes differentially increased yields up to 779% and discontinuous genotypes reduced yield after crop maturity up to 85% for determinate genotypes. Five months after planting (150 DAP) is proposed as the ideal scoring time for this scale, however, there is need for further   I still find lots of details that obscure the data and the important information of the paper. It looks a little better but not quite completed in a way that can allow readers to capture the science and potential applications expected from this study. Some parts are still difficult to read since they do not connect the data with the narrative and the findings. In some cases are repetitive concepts scattered in diverse places.
The number of figures and tables are too many, 18 in total. In particular I do not think you need that much information.
I have checked some parts of the paper and have placed some examples of how the authors can synthesize information. My suggestion is that the authors go again all over the manuscript and work on organizing information in a simpler way. Because of the practical value in sweet potato agriculture and sustainability, this study should be easy to read for large audiences in diverse areas.
To start please note an example in one part of the abstract below. Everything highlighted in bold provides the information. Words between parenthesis are not needed since you can explain those details in results and discussion. I also added some wording suggestions in italics ---.... (in GenStat Data analysis was done using residual or restricted maximum likelihood (REML). 18th Edition). (There were strong linear and quadratic trends over time (P<0.001) indicating a) Data showed non-linear growth pattern within and between locations, seasons, and genotypes for (a determinate linear) most traits. Some genotypes displayed early initiation and increase of (following a quadratic growth pattern).

bulking, while others showed late initiation
Broad sense (would be) (, heritability of CSRFAB was low due to large GxE interactions probably but higher in however, it was relatively high (50.5%) compared to other yield related) traits other probably due to (indicating) (accuracy of the high genetic influence and the effectiveness of the methodology developed method to quantify yield overtime)....

In Experimental Design:
The plants of each of the 130 sweet potato genotype were propagated as clonal forms or segregating seeds?
In Results and Discussion: In Growth pattern analysis and phenotypic variability across selected CSRFAB sweetpotato traits ---What do you mean when you said ....For most of the traits, their three-way interactions were significant . How is which means in reality that at least one of the 2-way interactions changes across the third factor this important in the discussion? Also it seems to be a contradiction saying that there was high genetic variability across locations and the need to study stability. Please clarify.

In Variance component analysis and heritability estimates of selected sweetpotato growth traits in a nonlinear model structure
You said the same in two parts of the same paragraph .... "This implies that the scale used to measure the Heritability was relatively high for . trait is precise and the trait is not influenced much by the environment SRNO (67.2%) and CSRFAB (50.5%). The low residual variance and high heritability observed for CSRFAB imply that the CSRFAB trait in this study was not greatly influenced by environment and the scale used to measure the trait is precise" Genetic effects on yield of CSRFAB genotypes in sweetpotato: I placed below another example on how you can be more straightforward in providing a more : "...We performed a harvest basis analysis and calculated the clear description of your results corresponding heritability for each trait to understand the dynamics of breeding values across harvest times. We targeted the traits that can affect final yield. Thus, the genotypic variances among seven main traits associated with CSRFAB varied from 0 to 72.46 ( ). Genotypic variance was high for HI at Table 6 120 DAP (72.46) and VY at 180 DAP (59.91) ( ). Zero variance was recorded for VY (120 DAP), HI Table 6 (150 DAP), SEN (180 DAP) and weevils (180 DAP). The zero (or negative) variance could be due to an artifact of the algorithm or competition effects between adjacent plots. These results imply no significant change in VY, HI, SEN and weevil damage within population for the respective growth periods. Since heritability is influenced by genetic variance, it was directly affected at the same traits and harvest periods. (ranging from 0 to 67%)..." The paragraphs below are highly speculative and possibly wrong if you assume your comments on artifacts in the algorithm. You need to support this better ---The 0% heritability implies that the effect of the traits is moving to fixation, that is, its frequency in the population is close to 100%. If we score the effect of the allele (s) regulating the trait having no heritability in the population, the result would mean in genetic terms, that the allele frequency in the population is 100%, therefore the genetic variance at the loci of these genes is zero, so any variance in the corresponding phenotypic traits cannot be attributed to .and the next paragraph. the non-existent genetic variance..

In: Identification of storage formation and bulking growth patterns and their characterization:
showing what is truly relevant with respect to the variation in traits assessed to explain CSRFAB and the variation among genotypes. This manuscript is too long.

Some additional comments:
In Introduction: The second paragraph, it seems to me that the authors should clarify better if yield increase was because of the effect of breeding or just an increase of planting areas. If the latter this doesn't help much justifying the addition of new breeding forms.
In Methods: What is a mega-environment for selection? I assume it was as reference to environmental diversity but the term t is a bit misleading in my view.

mega-environmen
Results: It is not new to report that clonal forms can respond differently in different environments. Variation levels across multiple environments is often high in field experiments in any crop. How is this result original? Then, you indicate that there is high variability among genotypes across locations + seasons. Wasn't this expected from a variable set of genotypes?
When the authors indicate that "this can be explained by the large population effect combined with I would suggest clarification in that sentence since I understand the same number of sample size", plants and populations were used in the experiments. How were the effects compared on that basis?
The parameters that the authors have used are related to storage root development, it seems that indicating that genotype performance increases overtime and is linearly explained is a logical outcome and fully expected. Storage root formation is related to maturity and developmental stages (=time). Maybe you need to clarify that result and explain better.

If applicable, is the statistical analysis and its interpretation appropriate? No
Are all the source data underlying the results available to ensure full reproducibility? No source data required

Merits:
The work addresses an important issue of relatively low sweetpotato storage root yields in Sub Saharan Africa where the crop is considered an important component of food security. Therefore, any work that seeks to understand the biological and environmental constraints of storage root yield has merit for advancing the fundamental understanding of the problem and development of solutions to improve productivity.

Critique:
Although the premise of the study was reasonable, the manuscript has a number of general and specific concerns, chief among this is the apparent oversight of previous work on the subject of storage root formation. There is general scientific consensus of the definition of "storage root formation" within the context of storage root developmental stages. It is unfortunate that the manuscript failed to acknowledge this scientific consensus and how it could have informed the current work and led to the advancement of the understanding of storage root formation in general, and within the context of the study location in particular. A short list of such work is provided in the references section below. I do encourage the authors to review some or all of the listed references, particularly the work of Lowe and Wilson (1974 ). There are many similarities and parallels between the current work and Lowe and Wilson's methodology and approach. I do believe there is valuable data to be published from this research, but as it stands, I am recommending revision for the manuscript, as I believe that conceptual and structural changes need to be made on the manuscript. I hope that the following critiques will aid the authors in the refining of this manuscript to allow for revision in the future.
There has been prior work looking at abiotic and biotic constraints on early storage root development (between 30 and 50 days) wherein storage roots were anatomically examined to confirm storage root formation and then storage root number was used to measure the response (please see Solis (2014 et al. ) and Gajanayake (2014 ) below). A common thread in these prior reports is the confirmation of the et al. anatomical markers of storage root formation or lignification (generally lignified adventitious roots typically do not undergo storage root formation). These anatomical observations are relatively quick and require very basic tools and equipment. In contrast, the present work used visual assessments to indicate "storage root formation" or lignification (third paragraph and figure on page 6). For example, a score of 1 is based on visual observations of "no storage root initiation and no bulking." Unfortunately, as prior work will show, non-swollen or non-thickened adventitious roots cannot be visually classified as undergoing storage root formation or lignification without anatomical confirmation. As it stands, any analyses or models based on this scale is based on the assumption that a score of 1 or 2="no storage root initiation", which may not be accurate. Lowe and Wilson (1974 ) used the same approach as the current work and used an index to quantify thickened storage roots. They made very clear assumptions about their rating scale, in part as a result of their detailed anatomical work on storage root formation, as detailed in another study by Wilson and Lowe (1973 ). The authors are encouraged to review Wilson and Lowe (1974 ) and consider revising along these lines. I encourage the authors to reconsider using the "CSRFAB index" and instead use the quantitative measurements that they are already collected to describe the variation in storage root development and yield among the genotypes.
If there is one single problem that stands out, one that I find very unfortunate, is this statement found on the 2nd paragraph on page 4: "no study attempted to understand the storage root formation and bulking patterns overtime under field conditions." Lowe and Wilson (1974 ), referenced above and listed below, conducted such a study, not very different in its conceptual approach from the current work. Lowe and conducted such a study, not very different in its conceptual approach from the current work. Lowe and Wilson grew six sweetpotato cultivars in field plots, and subsequently sampled and assessed storage formation and storage root development at weekly intervals during the first 8 weeks of growth, and then at monthly intervals after 8 weeks, for up to 24 weeks. Lowe and Wilson were very keen to recognize that storage root formation can only be determined anatomically, so they developed an index to quantify storage root development based on thickness of roots.
Finally, I looked up Yanfu (1989) and was unable to find the data about storage root initiation et al. occurring as early as 7 days. At this stage, adventitious roots are typically still undergoing primary growth, even in high yielding cultivars. Please cite cultivar or genotype or primary source, if available. In addition, I am unable to locate the data on storage root number ranging from 30 to 112 depending on genotype. Please verify this citation. Perhaps this refers to 30 to 112 g/plant (average root yield)?

Discussion:
The research on which this manuscript is based has generated valuable data on the variability of storage root yields of the sweepotato genotypes in the collection and once the recommended structural and conceptual changes are made, the authors are encouraged to revise. Lowe and Wilson's (1974 ) work was titled "Comparative Analysis of Tuber Development in Six Sweet Potato (Ipomoea batatas [L.] Lam) Cultivars." Other than the unfortunate use of "tuber" in this case, perhaps the authors will take a cue from this very relevant prior work. Given that the subject of phenotypic variability of storage root development is under-researched in this geographic area, these findings will have undoubtedly have fundamental and applied merit.
However, if the authors insist on applying the term "storage root formation" in the context of their work, then they must implicitly define the contextual usage and exceptions relative to what the scientific community has adopted as the generally accepted conceptual and functional definition (please see a sampling of references below). In particular, they need to make the needed assumption that all non-swollen adventitious roots (Score 1) and not all slightly swelling adventitious roots (Score 2) are not undergoing storage root initiation and that anatomical examinations were not conducted. However, I strongly urge the authors to avoid this route as it does not advance our understanding of sweetpotato storage root formation and unnecessarily deviates from the scientific consensus.

Significant revisions have been made to improve on the flow; all changes have been highlighted.
One criticism is that the authors build the discussion including excessive statistics and excessive technical details of the statistical analysis. In many cases it was not clear what the findings were as information and concepts become unclear. I would advise the authors to make it simpler, you have to take into consideration that this paper will reach a broad audience with different backgrounds. This study has practical implications, giving a clear view of the results, discussions and analyses could enhance its outreach and practical application from breeding groups in the region.
Details on statistics and technical details have been drastically reduced while maintaining content wherever it makes the flow and the text easier to understand.
In summary, this manuscript needs major revision. It needs to simplify the statistical analysis to a point of showing what is truly relevant with respect to the variation in traits assessed to explain CSRFAB and the variation among genotypes. This manuscript is too long.

Major revisions have been made on the manuscript; methods section and experimental details have been modified and statistical analysis details reduced drastically.
Some additional comments: •In Introduction: The second paragraph, it seems to me that the authors should clarify better if yield increase was because of the effect of breeding or just an increase of planting areas. If the latter this doesn't help much justifying the addition of new breeding forms.
A modification has been made to the text: "The increase in production was due to a combination of factors varying in different countries but mainly due to increase in area and breeding efforts." •In Methods: What is a mega-environment for selection? I assume it was as reference to environmental diversity but the term mega-environment is a bit misleading in my view.
A modification has been made to the text to reflect environmental diversity; "mega-environment" has been dropped.
•Results: It is not new to report that clonal forms can respond differently in different environments. Variation levels across multiple environments is often high in field experiments in any crop. How is this result original? Then, you indicate that there is high variability among genotypes across locations + seasons. Wasn't this expected from a variable set of genotypes?
The confusing, rather long text has been deleted and the remaining text modified to refer to the CSRFAB trait.
•When the authors indicate that "this can be explained by the large population effect combined with •When the authors indicate that "this can be explained by the large population effect combined with sample size", I would suggest clarification in that sentence since I understand the same number of plants and populations were used in the experiments. How were the effects compared on that basis?
The details have been deleted and the remaining text modified for clarity.
•The parameters that the authors have used are related to storage root development, it seems that indicating that genotype performance increases overtime and is linearly explained is a logical outcome and fully expected. Storage root formation is related to maturity and developmental stages (=time). Maybe you need to clarify that result and explain better.
The terms used in the manuscript are "storage root formation" or "storage root initiation" and "bulking". Bulking is related to development and maturity. However, storage root formation or initiation, when continuous in the life of the genotype is a different component which has been identified in some genotypes in this study and can be exploited to benefit farming systems where there is piecemeal harvesting. A small modification has been made in the text by using both storage root formation and storage root initiation.
Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly

Modifications have been made under the Methods section for the reader to follow the design.
Are sufficient details of methods and analysis provided to allow replication by others? No The Methods and Analysis sections have been modified while avoiding expanding technical details.
If applicable, is the statistical analysis and its interpretation appropriate? No

Details of the statistical analysis and technical details have been reduced for clarity.
Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Partly The text in the Methods and analysis and results have been modified for smooth flow and clarity.

Competing Interests
No competing interests were disclosed.