Babuvirus and family Nanoviridae. It causes significant crop losses worldwide and also in Pakistan. BBTV is present in Pakistan since 1988 however, till now only few (about twenty only) sequence of genomic components have been reported from the country. To have insights into current genetic diversity in Pakistan fifty-seven genomic components including five complete genomes  were sequenced in this study. The genetic diversity analysis of populations from Pakistan showed that DNA-R is highly conserved followed by DNA-N, whereas DNA-U3 is highly diverse with the most diverse Common Region Stem-loop (CR-SL) in BBTV genome, a functional region, which previously been reported to have undergone recombination in Pakistani population. A Maximum Likelihood (ML) phylogenetic analysis of entire genomes of isolates by using sequence of all the components concatenated together with the reported genomes around the world revealed deeper insights about the origin of the disease in Pakistan. A comparison of the genetic diversity of Pakistani and entire BBTV populations around the world indicates that there exists a correlation between genetic diversity and recombination. Population genetics analysis indicated that the degree of selection pressure differs depending on the area and genomic component. A detailed analysis of recombination across various components and functional regions suggested that recombination is closely associated with the functional parts of BBTV genome showing high genetic diversity. Both genetic diversity and recombination analyses suggest that the CR-SL is a recombination hotspot in all BBTV genomes and among the six components DNA-U3 is the only recombined component that has extensively undergone inter and intragenomic recombination. Diversity analysis of recombinant regions results on average one and half fold increase and, in some cases up to four-fold increase due to recombination. These results suggest that recombination is significantly contributing to the genetic diversity of BBTV populations around the world.Banana Bunchy top virus (BBTV) is a multipartite circular single strand DNA virus that belongs to genus  Babuvirus in the family Nanoviridae [Pentalonia nigronervosa Coq, its sole known vector, and infects only the members of Musaceae [Banana bunchy top disease (BBTD) is the most common and devastating viral disease of Banana, predominantly found in Pacific and Asian regions. It has been considered one of the most important plant viral diseases around the world . In Pakioviridae . BBTV isMusaceae .Banana bunchy top virus (BBTV) is a multipartite circular single stranded (css) DNA virus comprising of six css DNA components each of about 1Kb in size , 6. BBTVOccurrence of genetic variations due to recombination is a well-established phenomenon in BBTV . This phIn the current study, we reported the sequence of complete genomes of five BBTV isolates originated from different districts of Sindh, Pakistan. The diversity analysis and putative recombination events were studied for Pakistani isolates and then for sequences available in the public database in GenBank. Also, the contribution of genetic diversity by recombination, recombination hotspots and population genetics are studied for BBTV genomes to better understand the evolution of this virus in various geographic regions around the globe.BBTV infected plant material showing typical BBTD symptom was collected from district Tandojam, Sindh province in 2006 for P.TJ1 isolate (for which DNA-R was previously reported by Hyder and colleagues (2007)), and in 2007, for P.BS1 & 2, P.GH1 & 2, P.HD1 & 2, P. JS1 ), P.KP1 & 2, P.MT1 & 2, P.NS1, P.TA1 & 2 from Tandojam, Bhitshah, Ghotki, Hyderabad, Jamshoro, Khairpur, Matiari, Nawabshah and Tandoadam districts of the Sindh respectively. The P.TJ3, P.NARC and P.Sakrand & P.TJ4 were isolated during 2011, 2017 and 2018 respectively from Tandojam district. The total genomic DNA extraction was performed using the CTAB method as described by Hyder et al., (2007). PCR amplification and DNA sequencing were performed with two pairs of adjacent outwardly extending abutting primer each specific for a different location in each genomic component. Using this technique multiple reads in sequencing every component was generated and the sites where one set of primer binds, were sequenced using 2nd pair of primer and vice versa. for each genomic component of BBTV .https://www.sequentix.de/software_dnadragon.php). Then the consensus of sequencing, originating from two different primer sets, was developed for each component of an isolate. The consensus sequence of each sequenced component from an isolate was submitted in GenBank. The accession numbers were , , , , ,  respectively.The PCR amplifications were carried out either using GoTaq® PCR Core System I  or using Taq DNA Polymerase (recombinant) (Fermentas UAB Lithuania), according to manufacturer’s instructions. A typical PCR reaction contained about 50 ng DNA template, respective Taq buffer, 1.5 mM MgCl2, 200 μM of each dNTPs, 2.5 units of Taq DNA polymerase and 50 pM of each primer. The thermal profile for both primer sets included pre-PCR denaturation at 94°C for 3 minutes followed by 35 cycles of denaturing at 94°C for 30 seconds, annealing at 52°C for 30 seconds and extension at 72°C for 45 seconds, and a final extension at 72°C for 20 minutes. The PCR products of P.TJ1 were ligated into pTZ57R  according to the manufacturer’s instructions and transformed into Escherichia coli DH5α , hsdR17, recA1, endA1, gyrA96, thi-1, relA1) cells by electroporation. While for those isolates sampled during 2007, the PCR products were ligated directly into the pGEM®-T Easy Vector  and cloned in E. coli DH5α cell. The clones were selected using 50 μg/ml ampicillin and screened for white colonies generated by insertional inactivation of functional beta galactosidase gene whose expression was induced by IPTG  converting chromogenic X-GAL  into blue color in non-recombinant bacterial cells. The plasmid DNA was extracted using a minipreparation protocol according to Sambrook and Russell , and conThe full-length sequences of all components of BBTV genome were obtained from NCBI GenBank using Taxonomy Browser till April 05, 2020 and given abbreviated names for convenience . The codhttp://tree.bio.ed.ac.uk/software/).To study the relationship BBTV isolates of Pakistan with BBTV isolates from other parts of the world, a Maximum Likelihood (ML) phylogenetic genomic tree based on the concatenated nucleotide sequences of entire genomic components  of those isolates for which full genomic sequences were available in the GenBank, was constructed with 1000 bootstrap replicates. In addition, individual component-based ML phylogenetic analyses of full-length sequences of each component separately was also performed in the Molecular Evolutionary Genetics Analysis program (MEGA), version X  with 100N/dS ratio, where dN represents the average number of nonsynonymous substitutions per nonsynonymous site and dS is the average number of synonymous substitutions per synonymous site. In MEGA X [N and dS values were estimated separately by using Nei-Gojobori method (Jukes-Cantor). The gene is under neutral selection when dN/dS ratio = 1, positive (or diversifying) selection when the dN/dS ratio is > 1 and negative (or purifying) selection when dN/dS ratio < 1.The selection pressure was estimated by dn MEGA X . dN and DnaSP version 6.12.03  was usedThe recombination analysis was performed using Recombination Detection Program (RDP) version 4 Beta14 . The intThe contribution of genetic diversity by recombinants in their respective populations was determined by selecting the recombined genomic regions for each recombination event in alignments of the respective component as identified by RDP and then selecting recombination population in the ML trees from the respective geographic location, aligning only the recombinant regions using MAFFT software and then calculating the genetic diversity range, π (Pi) and Watterson estimator θw (Theta-w) values as described earlier for the respective recombinant population with and without recombinant sequences.Total fifty-seven genomic components including five complete genomes of BBTV were sequenced from Pakistan during 2006 to 2018  from various BBTV isolates were obtained from GenBank using Taxonomy browser  and theiThe phylogenetic relationship of BBTV isolates from different banana growing regions was analyzed to determine the relationships of isolates from Pakistan with those from worldwide. For this purpose, a concatenated genome-based ML phylogenetic tree was generated. The tree includes a total of 132 BBTV genomes out of which 107 were from PIO and 25 were from SEA group. The phylogenetic analysis showed close homology of Pakistan isolates with each other making a single clade with close relationships with isolates from Congo, Sri Lanka, Rwanda, Malawi, Burundi and India with a bootstrap support of 78%  in PIO. N values (ranging from 0.001–0.062) for the coding region were smaller than dS (ranging from 0.002–0.352) and the dN/dS ratios (ranging from 0.031–0.612) were <1, indicating negative/purifying selection, except for DNA-U3 where dN/dS ratio was different between different geographic populations (N/dS ratio <1), while for Australian, Congo and USA populations showed positive/diversifying selection (dN/dS ratio >1) while population from India and Samoa showed neutral (dN/dS ratio = 1) selection pressure. The data of selection pressure indicated that the coding regions of all the components (except DNA-U3) of BBTV genome in various geographic populations are under purifying selection pressure. The purifying selection pressure is known to preserve the biological function and help in removal of deleterious mutations [To understand the evolutionary selection pressure on various BBTV genomic components, the nonsynonymous to synonymous substitutions rate per site were calculated  for codiulations . In Pakiutations , 60 indiThe DNA-U3 encodes U3 protein for which any function is not yet been determined, however, it is an integral  and esseMusa balbisiana, whereas isolates from the SEA group are found throughout the ranges of M. balbisiana and M. acuminata [M. balbisiana from the Indian subcontinent and M. acuminata from Southeast Asia [Similarly, the Indian subcontinent is noted as a major hub  of long-cuminata . Howeverast Asia , 64. Thiast Asia . Later, D values were calculated to determine the impact of demographic expansion and contraction in different BBTV DNA component populations. Negative values with statistical significance (P<0.02 or P<0.01) were obtained for DNA-R and DNA-C (Tonga population) and in the Australian population of DNA-S. These results were further confirmed by Fu and Li’s D and F statistical test values , reveals many recombination events of intergenomic (homologous recombination between the same components)  and intrTo understand the contribution of recombination in BBTV genetic diversity, the recombined regions identified by RDP for the respective events were selected and the diversity analysis of these geographic populations with and without recombinants was performed. The analysis  revealedThe frequency of recombined regions in BBTV genome was analyzed by plotting them on various components  using TJUnderstanding the genetic structure of virus populations and their evolutionary mechanisms is an important aspect of managing viral diseases . In PakiIn the present study to understand existing variability in BBTV population, a detailed analysis of genetic diversity of its all component was performed first for the Pakistani population and later for the entire population of BBTV in the world. The genetic diversity data of Pakistani population  suggesteThe genetic diversity of entire BBTV population around the world  was alsoP. nigronervosa ability to inoculate BBTV. Banana pseudostem is waxy and banana aphid prefers to feed in pesudostem so the differences in wax content or composition between the two cultivars may lead to observed differences in virus transmission in their studied varieties. Furuya and colleagues (2012) [The tendency of BBTV to induce genetic diversity is perhaps relevant for their ecological fitness. Selection pressure is an important estimator of evolutionary constraints imposed on coding regions . The dN/s (2012)  in a suss (2012) . BBTV iss (2012) , 71. Majs (2012) . Anothers (2012) . The pres (2012) , 80. TheTo evaluate natural selection at the population level significant negative values of neutrality test in different BBTV components  suggest Recombination is a major contributor to genetic diversity , 74 and The contribution of genetic diversity by recombination  revealedgeminiviridae [The frequency of recombined regions in BBTV genome undergoing recombination  involvediviridae , 20, 72 iviridae  through iviridae , the CR-Conclusively, five full genomes have been sequenced from BBTV infected banana plants from Pakistan. Deep insight into the genetic diversity analysis of Pakistani and entire BBTV populations around the world showed some interesting contradictions in the diversity of functional regions which might be attributable to recombination. This study highlights the benefits of characterizing complete BBTV genomes rather than focusing on individual components from Pakistan. This analysis also complements the facts that different BBTV genomic components are diversifying at different rates and within one component, different parts have different levels of divergence. These differences suggest that geographic factors are also playing role in shaping the diversity and evolution of BBTV. Recombination analysis showed DNA-U3 a recombination hot component while CR-SL a recombination hotspot in BBTV genome. This in certain cases leads towards about four-fold increase in genetic diversity in the recombined population.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.


Intensive land use (ILU) is a multi-objective optimization process that aims to simultaneously improve the economic, social, and ecological benefits, as well as the carrying capacity of the land, without increasing additional land, and evaluation of the ILU over long time series has a guiding significance for rational land use. To tackle inefficient extraction of information, subjective selection of dominant factor, and lack of prediction in previous evaluation studies, this paper proposes a novel framework for evaluation and analysis of ILU by, first, using Google Earth Engine (GEE) to extract cities’ built-up land information, second, by constructing an index system that links economic, social and ecological aspects to evaluate the ILU degree, third, by applying Geodetector to identify the dominant factor on the ILU, finally, by using the S-curve to predict the degree. Based on the case study data from northern China’s five fast-growing cities , the findings show that the ILU degree for all cities has increased over the past 30 years, with the highest growth rate between 2000 and 2010. Beijing had the highest degree in 2018, followed by Tianjin, Zhengzhou, Jinan, and Shijiazhuang. In terms of the time dimension, the dominant factor for all cities shifted from the output-value proportion of secondary and tertiary industries in the early stage to the economic density in the late stage. In terms of the space dimension, the dominant factor varied from cities. It is worth noting that economic density was the dominant factor in the two high-level ILU cities, Beijing and Tianjin, indicating that economic strength is the main driver of the ILU. Moreover, cities with high-level ILU at the current stage will grow slowly in the ILU degree from 2020 to 2035, while Zhengzhou and Jinan, whose ILU has been in the midstream recently, will grow the most among the cities. These problems are especially severe in northern China2. In this context, the shortage of land resources has gradually become a key factor restricting urban development. In order to optimize the land use structure and improve the land use efficiency, intensive land use (ILU) has become an important strategy to alleviate many conflicts, including the conflict between people and land. It is also an inevitable choice for the development of urbanization in China3. Specifically, the ILU refers to meeting the appropriate scale of urban development under the current conditions, taking the rational layout of the city, optimization of land use structure and sustainable development as the premise, and continuously improving the land use efficiency and achieving good economic, social and ecological benefits through increasing the element investment level of the unit land area and improving the land management7. The definition shows that the ILU is a complex process and assessing it requires knowledge from economics, social sciences, urban planning, geography, ecology, and statistics, demonstrating its interdisciplinary character. Therefore, this complexity and the inherent interdisciplinary characteristics of the ILU determines the difficulty of the comprehensive evaluation. How to scientifically evaluate and analyze the ILU, to provide references for Sustainable Development Goals (SDGs) has become a hot topic in recent years9.Since the 1990s, China has experienced massive population growth and rapid socio-economic development, resulting in numerous challenges, particularly land problems12. From the perspective of temporal scales, the evaluation scales cover time nodes and time series. Meanwhile, a rational and scientific ILU evaluation index system serves as the foundation for accurately evaluating the ILU degree in a city. The evaluation system often relies on spatial scales and research objects. However, the evaluation involves many indices, including both socio-economic and natural types, so it is also vital to select indices with reliable and easily accessible data sources. The traditional evaluation of the ILU degree frequently relies on statistical information, which has some drawbacks in extracting the index data required for the evaluation13. For instance, some index data was missing due to incomplete statistics in the early years, and some data from different years had different statistical scales. Because of the difficulties in extracting reliable index information, evaluating the degree in long time series is challenging.Recently, a considerable amount of literature has grown up around the theme of evaluation for ILU. From the perspective of spatial scales, the evaluation scales in existing studies are various, mainly including national, provincial, municipal, and village regions, as well as specific land use types such as industrial land and educational land14. Unsupervised algorithms include fuzzy C-means algorithm, K-means algorithm and ISODATA15. As they do not allow expert knowledge input and their classification results are challenging to match with the actual situation, they are not commonly selected for use. While the main representatives of supervised algorithms are support vector machines (SVM) and maximum likelihood classifiers (MLC)16. They also have some drawbacks. For instance, SVM requires complex parameter settings before classification, while MLC can only perform more accurately if the spectral data satisfy a normal distribution. To address these challenges, scholars have proposed more advanced algorithms15, such as Random Forest (RF) and Classification and Regression Trees (CART). Among them, RF is currently the most popular algorithm, and this is because it can achieve higher accuracy than the above algorithms and require less parameter tuning17. Hence, we selected RF as the classifier in this paper. However, the traditional classification process often relies on the local computer, which requires downloading satellite images to the local computers and configuring the software environment. The operation steps are relatively complicated, and the classification efficiency is primarily dependent on computer performance. The Google Earth Engine (GEE) platform, combines cloud computing power with massive multi-source satellite data and can efficiently investigate scientific topics at various spatial and temporal scales20. It is valuable to map land use effectively and provides a chance to solve the problems listed above. Hence, using the GEE platform to extract land use information in conjunction with existing reliable socio-economic data is helpful for efficient ILU assessment.With the development of satellite remote sensing, carrying out research on ground objects  has already been possible under the long time series, so making full use of information extracted from satellite images for evaluation can effectively alleviate these problems from statistical data. At present, the application of machine learning (ML) algorithms to land use classification has attracted much attention. ML algorithms can be divided into two main subtypes: unsupervised and supervised algorithms23, such as correlation analysis, partial least squares regression, and multivariate regression analysis. However, given the spatial heterogeneity of geographical phenomena and the complex process of the phenomenon response to the influencing factors, the inflexible linear models are unlikely to accurately reveal the inner mechanism between the factor and the phenomenon, and the presence of exogenous covariates  can also limit the usefulness of the model24. To address these issues, Geodetector, based on the theory of spatial stratified heterogeneity, was proposed to measure spatial stratified heterogeneity and explore the driving forces of factors on the phenomenon25. As a result, Geodetector offers an opportunity to explore the dominant factor of the ILU degree.Analysis of the dominant factor affecting the ILU is essential for understanding the inner mechanism and operation law of ILU in cities. Notably, many approaches reveal the driving forces underlying geographical phenomena26 developed a theoretical framework to predict cities’ properties as a series of scaling relations applicable to all urban systems. This finding inspires scholars to find suitable functions to predict the cities’ properties in the future. Thus, the key to predicting the ILU level is to find a suitable function. Moreover, it is worth noting that many studies28 have shown that urbanization evolution in a city can be expressed as an S-curve; this curve shows an initial slow growth followed by rapid growth and finally a slow growth again. As an indirect indicator to assess the quality of the urbanization evolution, the ILU degree can also theoretically be modeled using the S-curve. This finding will provide references for subsequent ILU prediction studies and give better scientific support for land use optimization.In addition, the prediction of the ILU is significant in guiding future urban development. However, current ILU studies have mainly focused on the study area’s past ILU level. Specifically, scholars calculated the study area’s ILU in the past to analyze changes in the same region or differences between regions. Although prediction can better guide the ILU, few studies have focused on predicting the future ILU through the past ILU degree. Therefore, this paper explores the method of predicting ILU degree by drawing on forecasting studies related to cities. Specifically, BettencourtBased on the above literature review, we can find the problems in current ILU evaluation studies, including difficulties in extracting index information under long time series, subjective analysis of the dominant factor on ILU, and lack of prediction. To address these issues, this paper proposes a research framework Fig.  for eval2, with a population of 21.54 million and a GDP per capita of 23,135 USD in 2018. Tianjin is the largest port city and economic center in northern China, and it covers 11,903 km2, with a population of 15.59 million and a GDP per capita of 18,242 USD in 2018. Shijiazhuang is the political, economic, scientific center of Hebei and a commercial port in northern China. It has an area of 14,530 km2, with a population of 10.95 million and a GDP per capita of 8421 USD in 2018. Jinan is located in the middle of Shandong, a famous national historical and cultural city in China. It covers 7998 km2, with a population of 6.55 million and a GDP per capita of 16,064 USD in 2018. Zhengzhou is the core city of the Central Plains City Cluster, an important national integrated transportation hub. It covers 7509 km2, with a population of 10.13 million and a GDP per capita of 16,111 USD in 2018.Northern China’s fast economic development and urbanization have spurred population expansion and exacerbated the tension between land supply and demand over the past 30 years. In order to encourage high-quality urban development, it is important to choose representative cities for ILU evaluation and analysis. Hence, we considered the northern cities' economic, demographic, and geographic conditions, and selected five cities with high overall strength, including Beijing, Tianjin, Shijiazhuang, Jinan, and Zhengzhou Fig. . They plhttps://earthengine.google.org). Due to the long time series involved in this study, TOA data from the Landsat 5 satellite and the Landsat 8 satellite were employed, both of which contain common bands of Blue, Green, Red, NIR, and two short-wave infrared bands (SWIR1 and SWIR2) with a spatial resolution of 30 m. We selected the image data from June to September as the original image set. In order to make the most of all acquired images and create the annual cloud-free Landsat composites at each location, we applied the algorithm “simpleCloudScore” for the TOA data, and the least cloudy pixels were taken as the composite value. The algorithm can evaluate the “cloud score” by integrating indices such as the Normalized Difference Snow Index (NDSI), the brightness and temperature from the Landsat TOA reflectance data30. Finally, we successfully generated the least cloud images covering the study area from 1990 to 2018.In order to extract the information of built-up land in the study area, we utilized the time series of Landsat Tier 1 Top-of-Atmosphere (TOA) reflectance data acquired for 1990, 1995, 2000, 2005, 2010, 2015, and 2018 via GEE , which has a unique capability to detect visible and near-infrared emission sources at night with a spatial resolution of 30 arc seconds. The “stable lights” band was used, as it has been processed to discard transient events such as fires, so the background noise is discarded and replaced with zero values. Since the DMSP/OLS data is not available for 1990, we used the closest data to 1992 as a proxy. For the period 2014–2018, we used the VIIRS Day/Night Band Nighttime Lights dataset (version 1), which is composited monthly at a resolution of 15 arc seconds and uses a procedure to correct for stray light. The “avg_rad” band that represents average Day/Night Band radiance values was used. Meanwhile, to maintain consistency in spatial resolution between the two products , we projected them to Lambert Conformal Conic projection and resampled them to 500 m.In this study, the land use information required for the ILU evaluation is mainly the built-up land information, so we only mapped the “built-up” land and “not built-up” land during the classification process. The NTL data were used to select “built-up” and “not built-up” labeled examples31. As pixels with high brightness are associated with artificial structures that emit light, we assume that pixels with DN values above the threshold can represent areas with built-up land. Here, we illustrate the operation steps of Beijing’s NTL data in 2018 as an example, with the following: first, the distribution of DN values was created by using a histogram in GEE, then the cumulative frequency was calculated based on the histogram. By referring to the empirical thresholds31, we divided the cumulative frequency into three parts: 0–75%, 75–95%, and 95–100% 32, we got its threshold for identifying vegetation (water bodies). If a pixel’s NDVI (MNDWI) value is higher than the threshold, we identified it as vegetation (water bodies). Then the two types were removed according to the thresholds. Figure Since the study area contains five cities, manual selection of sample sets would be a time-consuming task. Instead, we applied a new sample selection strategy based on the NTL data, which requires minimal manual inputFor the subsequent classification of built-up land from 1990 to 2018 for each city, 1200 samples were generated for each year, with a total of 8400 for each city over the seven years . 70% of the samples were used for training, and the remaining 30% were used for validation.9 and the conditions of the study area. Table Given that land is the spatial carrier of urban economic, social, and ecological systems, and also combined with the definition of ILU, it is clear that ILU is not only about high-intensity input and high-efficiency output, but also the unification of economic, social and ecological benefits, ultimately achieving the coordinated development of economy, society and ecology. Therefore, this paper constructed the evaluation index system for the ILU degree based on three aspects: economic, social and ecological. We adequately selected representative and accessible indices by referring to related studies33. Furthermore, RF is a machine learning algorithm built into the GEE platform. As a result, we chose RF to extract the built-up land. Based on the experiences of previous studies35 and data collected, we set ntree to 500, and mtry was set to the default value . Next, we selected nine features as input features, specifically, five spectral features, i.e., gray values of Blue, Green, Red, NIR, and SWIR1 bands were selected, one textural feature, namely Geary’s C coefficient36 was chosen, three spectral indices, including NDVI37, MNDWI38, and NDBI39 were singled out. Table Since the RF algorithm consists of multiple decision tree classifiers, it outperforms other popular algorithms in many application scenarios . Up to now, RF is one of the most widely used algorithms for land cover classification by using satellite image data42 as alternatives to Kappa. Specifically, the quantity disagreement describes how well the mapped products represent the area covered by each class, and allocation disagreement refers to the spatial agreement between mapped areas versus the validation data. More details of these metrics can be found in the published studies44. The equations of OA and Kappa are as follows:xii is the number in row i and column i of the matrix, x is the total number of validation samples, and xi* and x*i are the total number of samples in row i and column i, respectively.The validation process was carried out based on the selected samples using the NTL data and Google Earth. Several accuracy assessment metrics were calculated to assess the accuracy of the extraction. Traditional accuracy assessment often uses overall accuracy (OA) and the Kappa coefficient to indicate whether the classification results are good or bad. However, a growing number of scholars have accepted that the Kappa coefficient has significant limitations and is somewhat misleading, so we also calculate the quantity and allocation disagreement45 to calculate the corresponding indices’ weights. The steps are as follows:j-th index for the i-th city in the λ-th year can be expressed in terms of xλij . Equation .Calculate the entropy value of each index, where Calculate the redundancy of the entropy values for each index and the corresponding weights.Calculate the ILU degree for each city in each year.As many methods of determining index weight are susceptible to subjective factors, the entropy weighting method determines weights based on the dispersion of index values, which is relatively objective. This paper refers to the steps of the previous studies25. The method is based on the assumption that if the independent variable has a significant influence on the dependent variable, the independent and dependent variables’ spatial distribution should be similar. More specifically, according to spatial distributions of these data, the method not only can quantify the relationship between two variables, Y  and X (influencing factor), but it can also explore the interaction relationship between two influencing factors (X1 and X2) to the geographical phenomenon Y, without assuming linearity of the association. In this paper, we mainly used the function module Factor Detector of Geodetector to reveal the influencing mechanism of the study, which measures the variable X’s explanatory power to the variable Y through q value, shown in Eq.  to solve the parameters a, b . The proportion of secondary and tertiary industries output-value (q = 0.86) was the dominant factor impacting the ILU between 1990 and 2000, suggesting that the rise in the ILU of cities was mostly impacted by the upgrading of industrial structure. After 2000, however, economic density (q = 0.97) became the dominant factor, indicating that the growth during this time was attributable to the strengthening of the economic level of cities and the economic output efficiency of the land.Exploration of the spatial dimension.The dominant factor may vary not just through time but also from city to city. Table In order to explore the dominant factor affecting the city’s ILU degree in depth, we used the index system’s indices as influencing factors, investigated the dominant factor in time and space dimensions by using Geodetector. The factor with the largest q in each detection was considered as the dominant factor.2 and RMSE. R2 of all cities is greater than 0.95 except for Shijiazhuang, below 0.9, and RMSE of all cities is lower than 0.05. These evaluation parameters indicate that the forecast results are indicative on the whole.Using the S-curve to predict the ILU degree, the results of the five cities are shown in Fig. In terms of time point, Beijing will still have the highest degree of ILU at 0.945 by 2035, followed by Tianjin (0.922), Zhengzhou (0.836), Jinan (0.808), and Shijiazhuang (0.617). This ranking is in the same order as in 2018. The results for Beijing and Tianjin tend the S-curve from 1990 to 2035. While Shijiazhuang, Jinan, and Zhengzhou show a trend close to a line over the same period. By calculating the growth degree between 2020 and 2035, Jinan has the most growth with 0.217, followed by Zhengzhou (0.203), Tianjin (0.163), Shijiazhuang (0.140), and Beijing (0.133). Hence this ranking indicates that Jinan and Zhengzhou are likely to have significant increases in the ILU. In contrast, Beijing's growth will slow down in the future, and Tianjin is essentially similar to Beijing. Furthermore, Shijiazhuang's ILU degree in the prediction is low since it experienced fluctuations during 1990–2005, causing its growth stage to diverge from the S-curve trend.47. Hence, increasing the economic strength can ensure these measures are implemented and, as a result, raise the ILU level. The Geodetector results can also validate this hypothesis. Therefore, if cities with a low level of ILU want to enhance their ILU, they should learn from high-level ILU cities. For instance, cities such as Jinan, Zhengzhou cannot rely solely on control of the built-up land scale to raise the ILU level in the future, but also learn from the development experience of Beijing or Tianjin, such as promoting industrial transformation or improving technology and capital investment48, thus improving their economic strength and enhancing the development potential.The ILU development trend in the five cities is similar, but there are differences in the ILU degree. The direct reason for the differences is that different cities have different economic, social, or ecological strengths. However, the deep reason is that the dominant factor for ILU development differs between cities. Specifically, the five cities all show a general upward trend in ILU level over the last 30 years, and the growth rate of ILU in each city is greatest between 2000 and 2010. On the other hand, there are differences in the ILU between the cities. Beijing has the highest level of ILU at the current stage, followed by Tianjin, Zhengzhou, Jinan, and Shijiazhuang. In detail, Beijing, Tianjin, and Zhengzhou have the largest increases, mainly in the economic aspect, while Shijiazhuang’s economic or social growth degree is far below its ecological growth degree. Jinan is a different story. Its growth is strong in both economic and social aspects. Thus it is clear that cities making economic progress are more likely to achieve higher ILU degrees. This is due to the fact that ILU is an optimization progress without increasing additional land. This process requires measures such as increasing building density, technology, and management capabilities, and these measures all need a significant capital investmentCities with a high level of ILU will slow down in the future, whereas cities with medium to low levels of ILU will see a significant increase in ILU. Concretely, we can see that Jinan and Zhengzhou will likely achieve a significant increase in ILU degrees during 2020–2035, while Beijing and Tianjin's growth will slow down. Beijing has reached a high level of ILU at the current stage, and Tianjin is broadly similar to Beijing. Shijiazhuang's ILU degree in the forecast is not high because it has experienced fluctuations during 1990–2005, which may prevent S-curve from predicting better. Combining results of time nodes with the growth of periods, we can anticipate that, though there will still be a difference in ILU level between the cities in the future, it should be lower than it is now because results suggest that cities with high ILU at this stage will enter a slow growth phase. In contrast, cities with relatively low-level ILU, which currently have more development potential, will enter a period of rapid growth. Thus the gap between cities will gradually narrow.The uncertainties that exist in this study will have an impact on the study’s accuracy. First, the classification results of the Landsat images cannot be completely accurate, introducing uncertainty into the following ILU evaluation. Specifically, there are different types of uncertainties in the various operations from data acquisition, pre-processing, and classification in the classification process. Therefore, this paper attempts to minimize this uncertainty by standardizing the entire classification process as much as possible and ensuring the classification accuracy for each city is above 85%. Second, though the S-curve can predict the future ILU based on the past ILU degree of cities, it cannot avoid the influence of exogenous variables , and this uncertainty may have an impact on the model’s prediction. In order to mitigate the impact of this uncertainty, this paper avoids predicting the results too far in the future. Furthermore, this paper is based on the ILU's time-series data trend to forecast so that the forecast results can be in line with reality.The main contributions of this study are as follows: (1) It utilizes the GEE platform into extracting information of key indices for evaluating the ILU degree, solving the problem of inefficient and inaccurate extraction of information that has long plagued the evaluation process; (2) it constructs a relatively comprehensive evaluation system by taking three aspects into account: economic, social and ecological, and after calculating the ILU level, it uses Geodetector to detect the dominant factor affecting the ILU, thereby overcoming the shortcomings of subjective selection of the dominant factor in the evaluation; (3) it innovatively uses the S-curve to predict the ILU degree in the future, enriching the theoretical methods to anticipate the ILU.There are also a few limitations that require further research. One of them is that the number of cities selected in this study is relatively small, which may limit our findings on the spatial characteristics of ILU. Another shortcoming is the limited number of indices selected for assessment in this study due to the limitations of data collection, and the selection can affect the evaluation results of ILU. Hence, in the future, to explore the characteristics of urban ILU at a larger spatial scale and to reveal more factors affecting the level of ILU, we will consider selecting more cities as the study area and including more indices reflecting the ILU. Furthermore, we intend to investigate the fusion and correction of the NTL data to extract the “built-up” land samples with greater accuracy.Based on the satellite data and the powerful cloud computing capability through the GEE platform, land use classification can be extracted efficiently. The increase in the area of built-up land is an inevitable trend in urban development, but the results of the built-up land and ILU degree in each city show that the built-up land area has an impact on the ILU, but it does not play a decisive role.Over the last 30 years, the ILU degree in each city has shown an overall increasing trend, with the highest growth rate occurring between 2000 and 2010. Among them, Beijing had the highest degree in 2018, followed by Tianjin, Zhengzhou, Jinan, and Shijiazhuang. Moreover, by analyzing the cities’ growth in economic, social, and ecological aspects of ILU, it can be concluded that cities with a greater increase in economic aspect are more likely to achieve a higher level of ILU.In terms of the time dimension, the dominant factor affecting the ILU of all cities changed with the city’s development. It shifted from the output-value proportion of secondary and tertiary industries in the early stage to the economic density in the late stage. In terms of the space dimension, the dominant factor varied from cities. In Beijing and Tianjin was economic density, and in Jinan and Zhengzhou was development intensity, and that of Shijiazhuang was population density. By referencing high-level ILU cities , it is evident that improving the economic output efficiency of land and the city’s economic strength is the best way to promote the ILU degree at this time.There will still be a difference in ILU levels between these cities. Cities with high ILU degrees at the current, such as Beijing, would have their ILU increase slowly from 2020 to 2035, while Zhengzhou and Jinan, whose ILU degree has been in the midstream recently, their degree will grow the most among the cities during 2020–2035. Although the ILU level will still vary between cities in the future, this variation should be less than at this stage.This paper developed an index system based on the economic, social, ecological aspects, rapidly extracted built-up land information with GEE support, and used the entropy weighting method to evaluate the ILU degree by combining socio-economic statistics. Then, we used Geodetector to explore the dominant factor and S-curve to make predictions on the ILU degree, completing a whole framework for the ILU research that included the extraction of information, calculation of the ILU degree, detection of the dominant factor, and prediction. The findings are as follows.As the conclusions show that the built-up land area is not decisive for the ILU, we recommend that the government strictly control the total amount of built-up land in cities to avoid excessive urban sprawl, and the ILU should be increased by improving the land use efficiency. Specifically, different cities should determine the development strategies according to their current ILU level. For the high-level ILU cities , which are stronger in economic aspect but weaker in social and ecological aspects. These cities should maintain their economic strength while improving their social and ecological strengths, such as increasing road density and urban green spaces. For the low-level ILU cities , their economic, social, and ecological strengths are not outstanding. The Geodetector results show that improving the economic strength is an important way to improve their ILU level rapidly. Therefore, these cities should increase their economic density by utilizing fewer land resources to support higher quality and more efficient economic development. By implementing these measures, these cities will achieve a rapid increase in the ILU in the future, as predicted by the results.


Dioscorea spp.) is a staple crop for millions of people in the tropics and subtropics. Its genetic improvement through breeding is being challenged by pre-zygotic and post-zygotic cross-compatibility barriers within and among species. Studies dissecting hybridization barriers on yam for improving the crossability rates are limited. This study aimed to assess the cross-compatibility, which yielded fruit set, viable seeds and progeny plants in an extensive intraspecific and interspecific crossing combinations in a yam genetic improvement effort to understand the internal and exogenous factors influencing pollination success. Cross-compatability was analyzed at the individual genotype or family level using historical data from crossing blocks and seedling nurseries from 2010 to 2020 at the International Institute of Tropical Agriculture (IITA). The average crossability rate (ACR) was lower in interspecific crossing combinations (6.1%) than intraspecific ones (27.6%). The seed production efficiency (SPE) values were 1.1 and 9.3% for interspecific and intraspecific crosses, respectively. Weather conditions and pollinator's skills are the main contributors to the low success rate in the intraspecific cross combinations in yam breeding. At the same time, genetic distance and heterozygosity played little role. Interspecific cross barriers were both pre-zygotic and post-zygotic, resulting from the evolutionary divergence among the yam species. Dioscorea rotundata had higher interspecific cross-compatibility indices than D. alata. Distant parents produced intraspecific crossbred seeds with higher germination rates compared to closest parents . This work provided important insights into interspecific and intraspecific cross-compatibility in yam and suggested actions for improving hybridization practices in yam breeding programs.Yam ( Dioscorea spp.) is a crucial crop for food security and poverty alleviation in the tropics and subtropics1. Of the ~ 600 yam species, eleven are majorly produced for food and income2. Yam globally ranks fourth among the root and tuber crops and second after cassava in Africa3. In West Africa, where yam is extensively grown , the per capita consumption can be as high as 40 kg per year for some ethnic groups4. In this region, the yam crop is an integral part of the socio-cultural and religious belief systems. Despite its importance as a staple food crop, source of income5, and socio-cultural connection to the society which depends on it, yam productivity has not increased much as compared to other root and tuber crops or cereal and grain crops6. Yam cultivation is challenged by many factors, of which an under-developed seed system is primary. In response to this significant challenge, there are breeding and seed system initiatives to improve the quality and availability of improved varieties and their seeds.Yam  are unique sources of genes for these traits and thus should be exploited for yam breeding progress2 estimated the average crossability (ACR) and seed production efficiency (SPE) rates at 20.3 and 10.5%, respectively, for D. rotundata and 28.0 and 9.3%, respectively, for D. alata at the IITA, Ibadan, Nigeria. Although several reports referred superficially to the pollination success, cross-compatibility, and seed viability among and within yam species, very few studies entirely focused on these aspects. A retrospective analysis of the past pollination practices and information is crucial for a better understanding of factors linked to the reported low cross-compatibility rates in yam breeding and to suggest future directions. This study, therefore, explored the yam pollination information at the IITA for the period of 2010–2020 to specifically assess: (1) the cross-compatibility rates among and within yam species; (2) the germination rate of crossbred seeds, and (3) internal and external factors associated with yam cross-compatibility rates.The International Institute of Tropical Agriculture (IITA) is a member of the Consultative Group for International Agricultural Research (CGIAR) with the global mandate for yam research. In partnership with its national and international partners, IITA maintains, develops, and releases yam varieties to meet farmers’ and other end-users needs and demands. However, past experiences in yam breeding revealed consistently low intra- and interspecific cross-compatibility rates throughout the years and stations. For instance, Darkwa et al.−1 and solar radiation of 14.94 MJ m−2 day−1 . The two stations were significantly different for maximum temperatures , minimum temperatures , and wind speed . All other parameters showed no differences between the two stations.At the Ibadan station, the temperature was stable across years while the rainfall was erratic and significantly fluctuated from year to year Fig. a. The yeD. rotundata and D. alata crossing blocks, respectively  and D. alata (9.3%) had comparable seed production efficiency (SPE) values.Between 2010 and 2020, the IITA yam breeding program made ~ 332,500 and 176,645 hand pollinations in D. rotundata when crossed with its wild relatives, D. burkiliana (29.2%) and D. hirtiflora (27.3%). Interspecific crosses involving D. alata had consistently lower ACR (Table D. rotundata × D. burkiliana (9.7%).The highest interspecific ACR were recorded on rkiliana 9.2% and D. rotundata and D. alata crossing blocks  significantly influenced the pollination outcomes in either the ks Figs. . In Auguata Fig. .Figure 1D. alata and D. rotundata pollination blocks, respectively.Technicians involved in yam hand pollination also played a significant role in the cross-pollination success rates Table . Some teD. alata parental clones ranged from 0.02 to 0.84  and between the parental genetic distance and the SPE  or the parental genetic distance and the SPE  for D. rotundata cross-combinations .The .96 Fig. . No relaD. rotundata .The heterozygosity level of the parents had no significant effect on the ACR of the p < 0.001) of parent combinations on ACR and SPE for both major yam species  for D. alata and D. rotundata crossbred seeds. The highest seed germination rate was recorded in 2020 (54%), while the lowest rate, 37%, was in 2015. The seed germination rate was significantly different among the supervised interspecific and intraspecific crossbred seeds and the unsupervised open-pollinated intraspecific crosses (p < 0.001) (Table D. rotundata germinated most (61%), while that of D. alata were least viable (43%). However, intraspecific D. alata cross-bred seeds (55%) outperformed the D. rotundata intraspecific cross-bred seeds (49%). We noticed a positive correlation  between the seed germination rates and the parents’ genetic distance  Table . The ope26. Our retrospective analysis in yam crossing blocks at the IITA breeding program indicated that climatic factors and technician pollination skill are the main exogenous factors contributing to low cross-pollination success rates in D. alata and D. rotundata.This study used 11-year historical data to investigate intra- and interspecific cross-compatibility and seed germination rates in yam. We ascertained the generally low cross-compatibility rates among and within yam species reported by several studiesD. rotundata and November for D. alata could result in low pollination success due to the suboptimal weather conditions. This suggests the need for supplemental irrigation in yam crossing blocks to reduce the water deficit's adverse effects on yam reproductive phases during these months. Our results supported those of Abraham and Nair27, which linked successful pollination in yam with high relative humidity and moderate atmospheric temperatures.Among the climatic factors, weekly differences in rainfall, temperature, relative humidity, number of rainy days, and sunshine during the pollination window were the main contributors to success rates. Our results revealed that making crosses in August for p < 0.001). The anther removal from the male flower and its transfer to the female stigma is delicate. It requires special skills that might be lacking in many short-term pollinator staff involved in yam hand pollination operations. Pollinator's skill and weather conditions partly explained the significantly lower pollination success recorded at the IITA Abuja site than at Ibadan. Abuja site was characterized by lower relative humidity and higher temperatures compared to Ibadan. Hence, selecting conducive crossing sites and enhancing technicians' skills are highly relevant to improve the cross-pollination success rates in yam breeding programs.Examining technicians' performance on the same cross-combinations simultaneously, we found highly significant differences among technicians in making successful pollination  showed that ACR could be controlled by loci on chromosomes 1, 6 and 1712. That study, therefore, opened an avenue for developing genomic tools for predicting hybridization success in yam breeding programs.Although no significant effect of genetic relatedness and heterozygosity of crossed parents was established on cross-compatility indices, there was an influence of parent combinations on ACR and SPE for both major yam species. Using the same 7, several other internal and exogenous factors seem to influence yam pollination successes and should be given attention in future studies. Factors such as the optimum pollination time, floral morphology, and biology, especially for anthesis detection, pollen parent viability, etc., should also be investigated to identify additional causes of low intraspecific pollination success in yam at IITA. To complement this study conclusions, we recommend a more refined experiment on ploidy status among and within species to elucidate its effects on intra and interspecific cross-pollination success.As previously suggested by Mondo et al.D. rotundata) and water  yams involved six relative species at IITA. Those relative yam species served as valuable sources of genes for yam mosaic virus (D. praehensilis), bulbil formation ability as alternative planting materials (D. bulbifera), source of carotenoids (D. cayenensis), tolerance to nematodes (D. dumetorum) or as bridge between incompatible species (burkilliana and D. hirtiflora) . The same trend was observed on SPE as the highest values were on D. rotundata × D. burkilliana cross-combinations. This relatively higher cross-compatibility rate could be attributed to high genetic relatedness among these species, as reported by previous studies31. However, interspecific crosses involving D. rotundata with D. cayenensis had generally low success rates due to expected differences in ploidy level. Diocorea cayenensis is mostly triploid with male sex flowers, while diploid plants dominate D. rotundata with dioecious flowering patterns33. It is noteworthy that successful interspecific crosses were dependent on the cross direction such that reciprocals seldom ensured satisfactory results. This demonstrated the presence of unilateral incompatibility in yam species translated by rejection, in one crossing direction, of pollen from one species by pistils of a related species34. Therefore, when designing an interspecific hybridization program, male and female parents should be selected carefully to optimize the pollination success rates.Pre-breeding involving interspecific hybridizations is valuable for broadening the cultivated yam species' genetic base by introducing desirable genes. Past efforts of broadening the genetic bases of white (a) Table . Althoug19 associated such barriers to the degree of domestication. Besides, that pre-zygotic barrier, also referred to as pre-fertilization barrier, could be a result of the halfway arrest of the elongation of the pollen tube in the stylar canals18. On the other hand, post-zygotic barriers prevented embryo development and seed formation even though the fertilization was successful. Such post-zygotic barriers could be mainly attributed to differences in ploidy levels19 or by degradation of hybrid embryos and/or endosperm in incongruous crossings18. For successful interspecific yam hybridizations, both barriers need to be overcome. Direct in vitro pollination is among the techniques for overcoming the pre-zygotic incompatibility barriers in yams7. Besides, hybrid genotypes acting as a bridge between related incompatible yam species could be developed19 or to experiment the cut-style pollination technique which had been successful in other crops18. The post-zygotic barriers resulting in seedless fruits can be controlled by biotechnology techniques such as the embryo rescue to prevent post-zygotic abortions17. Such techniques have been fully developed and implemented by CIRAD-Guadeloupe, France, and could be transferred to other programs to exploit interspecific and interploidy crosses in yam breeding21. We have suggested a scheme illustrating how a successful interspecific hybridization can be designed in yam breeding programs  was rare in interspecific crosses. Luque et al.Dioscorea rotundata had higher seed germination under unsupervised open pollination than controlled hand pollination. Pollen donors of higher quality might have been involved in open pollination than those used in hand pollination35. This study showed also that distant parents produced highly viable crossbred seeds than closely related ones. This could be associated with the hybrid vigor of distant parents’ progenies. Fischer et al.35 argued that low genetic distance among crossed individuals reduces cross-compatibility and offspring fitness in species with a self-incompatibility system. This reduced offspring fitness could have translated into low seed germination and poor seedling vigor due to inbreeding depression. We also found seed germination rate improvement across years as a result of growth medium optimization at IITA since 2014. Most seedling nurseries have been using carbonized rice husk under proper management in screenhouse conditions36. Therefore, the seed germination rate is not only under genetic control but also influenced by growing media and conditions.This study ascertained the generally-reported low crossability and seed setting rates among and within yam species. Low success rates in yam crossing blocks were mainly attributed to the suboptimal weather conditions and technicians’ skill rather than the genetic relatedness and parents’ heterozygosity levels, as previously assumed. However, the seed germination rate was highest for crossbred seeds from genetically distant parents. Many other factors seem to influence the yam pollination success and should be investigated in future studies.Dioscorea. Dioscorea alata L., D. rotundata Poir., D. cayenensis Lam., D. burkilliana J. Miège, and D. praehensilis Benth. belong to the Enantiophyllum section. Dioscorea dumetorum (Kunth) Pax belongs to section Lasiophyton, while D. bulbifera L. and D. hirtiflora Benth are from the sections Opsophyton and Asterotricha, respectively. It is noteworthy that much of the yam breeding in IITA focused on the two most important cultivated species, D. rotundata and D. alata, with ~ 500,000 crosses recorded from 2010–2020. The list of most popular male and female D. rotundata and D. alata genotypes, their ploidy level, the type, the trait of interest, and average cross-compatibility rate information are provided in Table D. rotundata and the end of September to November for D. alata. Annual and monthly weather data are summarized in Fig. S1.In this study, 11-year (2010–2020) historical data generated from the IITA Yam Breeding Unit’s crossing blocks and seedling nurseries were explored. Information on intra- and interspecific cross-compatibility and seed germination rates was gathered irrespective of the breeding objectives. Eight yam species, sourced from IITA core collection, were used for inter- and intraspecific cross-combinations. These species are from different sections of the genus Historical data on the number of flowers pollinated, and the corresponding fruit and seed sets for the different cross-combinations conducted in IITA yam crossing blocks from 2010 to 2020 were used to calculate the cross-compatibility indices: ACR, PHC, and SPE. The cross-compatibility rate of a cross was calculated using the following formula:The ACR for a parent was calculated as the sum of cross-compatibility rates in specific crosses divided by the number of cross-combinations involving that particular parent:The PHC for a parent was calculated as the number of times the cross-compatibility rate exceeded the species overall cross-compatibility divided by the number of cross-combinations in which that parental genotype was involved:The SPE for a cross was calculated as the number of viable seeds divided by six times (the expected number of seeds in a yam fruit is six) the number of pollinated flowers multiplied by 100:37. This genetic relatedness analysis was performed using single nucleotide polymorphism (SNP) markers. This analysis involved 77 D. alata genotypes previously sequenced using Diversity Array Technology (DArT-Seq)38. SNP information of 302 D. rotundata genotypes was extracted from the genotyping by sequencing (GBS) studies reported in Bhattacharjee et al.39. The heterozygosity level of the different parental genotypes was then estimated using Tassel 540. It is noteworthy that analyses on genetic relatedness and heterozygosity were only performed on the two economically-important yam species, D. alata and D. rotundata, for which sufficient molecular information was available. Unlike the estimation of cross-compatibility indices which included historical crossing data of all the genotypes  maintained in the yam breeding at IITA, analyses of genetic relatedness and heterozygosity level only focused on popular clones  for which both sequencing and crossing information were available.To assess the influence of the genetic distance between crossed parents on ACR and SPE, Jaccard pairwise dissimilarity matrices were generated using philentropy package implemented in RThe crossbred seed germination rates were determined using sample data of 2014–2020. The seed germination rate was estimated by dividing the seedling stand count in nurseries by the number of seeds sown multiplied by 100:Seed germination rate was further associated with parents’ genetic distance information to assess the cause-effect relationship.−1), solar radiation (MJ m−2 day−1), minimum and maximum temperatures (°C), minimum and maximum relative humidity (%), sunshine (h) and the number of rainy days . The weather parameters records on a yearly, monthly, weekly, and daily basis were assessed using multiple regression analysis. Differences in meteorological parameters at the two crossing sites, Ibadan and Abuja, were assessed using a t-test after testing the data’s normal distribution using the Shapiro–Wilk test.We used ten weather parameters to assess their influence on the cross-pollination success in yam crossing blocks. The weather parameters assessed were rainfall (mm), evaporation (mm), wind speed . It is noteworthy that the two yam species’ flowering windows are spaced in time, June to early October for D. rotundata and the end of September to November for D. alata. Therefore, a different set of pollinators was used for each species since crossing activity is mostly performed by short-time staff.The pollinator effect was assessed on selected 2020 p-value threshold. The relationship of parents’ genetic distance with crossbred seed germination rate was also assessed using Pearson’s correlation analysis. Heatmap function implemented in package ‘qgraph’41 was used to estimate the genetic relationship among crossed parents. Results were presented as a heatmap with different color gradients based on genetic distance. Regression plots were then drawn to determine the level of significance and influence of genetic distance on ACR, SPE, and seed germination rate. Regression analysis was also used to assess the impact of parents' heterozygosity level on the ACR and PHC values. Pollinating technician’s effect on yam pollination success was evaluated using ANOVA and Tukey HSD All-Pairwise Comparisons Test at 5% p-value threshold.Historical data on ACR, SPE, and seed germination rate were summarized by means and standard deviations. Pearson’s correlation was estimated to assess the relationship between ACR and SPE with internal  and external (weather conditions) factors. Effects of the year, location, and cross-combination on cross-compatibility indices were determined using the analysis of variance (ANOVA). ANOVA was also used to assess differences in seed germination rates among crossbred seeds from interspecific, intraspecific and open-pollinations. Means were further separated by Fisher’s least significant difference (LSD) test at 5% Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Traditional fermented foods are of major importance with respect to the socio-economic growth, food security, nutrition, and health of African consumers. In several African countries, traditional fermentation processes provide a means of food preservation, improving the shelf life and adding to the nutrients in the food products. As with any fermented foods, the associated food microbiota is of great importance and interest. Recent studies on the microbiome of African fermented foods using high-throughput DNA sequencing techniques have revealed the presence of diverse microbial populations of fundamental, technological, and commercial interest that could be harnessed to further improve health, food safety, and quality. This review provides an overview of African fermented foods, their microbiota, and the health-promoting potential of these foods and microbes. These foods also enhance food security, sustainable development, and economic growth in Africa such as through the provision of employment opportunities, contribution to empowerment initiatives for unemployed women, opportunities for scaling up of traditional food processing techniques, and distribution of resultant products2. Fermented foods can be a rich source of nutrients, with fermentation bringing about a “pre-digestion” of food substrates to make the associated nutrients more bioavailable and, in some instances, removing allergens , antinutritional compounds  and toxins 3. The associated microbes can have health-promoting properties either directly, with some probiotic strains from most commonly consumed and extensively investigated fermented foods in Africa or indirectly, through the production of health-promoting metabolites. Several fermented foods also contain prebiotic components4. Indigenous fermentation processes represent traditional, reliable, and affordable methods of preserving nutritional and sensory qualities of associated substrates while extending shelf life and enhancing safety. Fermentation of these foods in several African countries usually involves spontaneous activities of natural microbes found in the food or from a previous fermentation, such as through “back-slopping” to bring about the desirable changes5. Fermentation of certain foods facilitates wet milling, the aforementioned removal of toxic or anti-nutritive compounds, and improvement of organoleptic properties and extension of the shelf life of the food products. The preservative effects of fermented foods are particularly important in Africa considering the tropical climatic conditions across large regions of the continents. The climate makes the preservation of fresh farm products such as milk, fruits, and vegetables very challenging, resulting in reports of over 70% loss of some products yearly7. Post-harvest spoilage of fresh foods is usually enhanced by environmental pollution and contamination which results in physical rotting, esthetic spoiling of the environment, and bad odor, ultimately making the food unsuitable for consumption. Although the preferred approach for the preservation of fresh foods is refrigeration, inadequate power supplies and low living standards in certain regions of Africa limit this option8. Thus, in some instances, food fermentation is the only option for preserving foods, resulting in products with enhanced acceptability, digestibility, functionality, and nutritional quality.Fermented foods play an essential role in tackling issues of poverty, malnutrition, and hunger among African consumersLeuconostoc, Lactobacillus, Pediococcus, Debaryomyces, Kluyveromyces, Saccharomyces, etc14, this is increasingly complemented by advances in shotgun metagenomic sequencing that, in addition to identifying the microorganisms present (including novel species and strains), can provide an insight into potential metabolic and functional pathways encoded18.Such enhancements are more controllable where there is a corresponding understanding of the microbiota of the fermented food. Thus, while, to date, there is a corresponding understanding of many of the functional pathways and metabolic potential of several fermenting genera including Fermented foods represent a significant component of the diverse range of African diets, with individual foods being assigned, for example, as staple foods, condiments, or beverages. The foods can also be classified according to the raw materials used for their production. These include tuber-based products ; cereal-based products ; legume-based products ; beverage-based products , and dairy-based products  Fig. . These rManihot esculenta), yam , and sweet potato (Ipomoea batatas) are common starchy foods. They are rich in carbohydrates and are generally cultivated for consumption in individual African households. Government policies in many African countries also support the production of cassava and fermented cassava products as an alternative to imported wheat and rice products19. Fermented cassava products commonly consumed across African countries include those produced from the tubers (fresh or dried), cassava leaves, milled paste or granulated fried products20. Tubers are usually peeled, washed, milled, and fermented for about 3–5 days into sour granulated flakes called “Gari” (Nigeria), steamed granulated flakes “Attieke” (Côte d’Ivoire) or milled into flour called “Lafun” or “Kokonte” (Nigeria and Ghana) to prepare meals such as “Amala,” “Eba,” and “Fufu” (Nigeria). Other fermented tuber products include “Agbelima” (Ghana), “Atangana,” “Kumkum,” “Myiodo” (Cameroon), “Maphumu,” and “Makaka” 23. The hydrogen cyanide (HCN), linamarin, loutaustralin, and other undesirable compounds in cassava are eliminated during processing which includes peeling, grating, soaking, pressing, and frying/cooking24. The cyanogenic glycosides are hydrolyzed by beta-glucosidases and HCN evaporates prior to consumption of the products25. Fermentation further eliminates the cryogenic glycosides through microbial activities (such as that by lactic acid bacteria (LAB) and yeasts) generating metabolites, including organic acids and volatile compounds, which contribute to the distinct organoleptic properties of the fermented products29. Several LABs have been isolated from African fermented tuber food products and found to produce antimicrobial agents, vitamins, and other compounds of interest. Microorganisms isolated from fermented tuber products include representatives of the species: Lactobacillus acidophilus, Levilactobacillus brevis, Limosilactobacillus fermentum, Ligilactobacillus salivarius, Limosilactobacillus reuteri, Lacticaseibacillus casei, Enterococcus faecium, Bacillus subtilis, Geotrichum candidum, Mucor racemosus, Saccharomyces cerevisae, Neurospora sitophila, and Rhizopus oryzae32.Tubers such as cassava (Panicum miliaceum), wheat (Triticum aestivum), maize (Zea mays), rice (Oryza sativa), and sorghum (Sorghum bicolor) and, as with other fermented foods, they undergo biochemical, and nutritional changes during the fermentation process34. The fermented cereal products can be in the form of a paste or slurry as in the case for “Ogi” (Nigeria), “Akasa” (Ghana), “Uji” (Kenya), “Abreh” (Sudan), “Mahewu” (Zimbabwe and South Africa) including fermented drinks like “Burukutu” (Nigeria) and “Kunnu” (Nigeria)36. The cereal-based fermented foods are reported as good sources of beneficial microorganisms, micronutrients and are regarded as health-promoting foods39. Fermented cereal products also contain LAB that contributes extensively to nutritional properties as well as to food safety, shelf-life quality, and organoleptic properties through the production of organic acids, bacteriocins, and volatile compounds. These LABs include Lactiplantibacillus plantarum, Limosilactobacillus fermentum, Levilactobacillus brevis, Lactococcus lactis, Leuconostoc species, and Pedicoccus species, while the other bacteria represented include Streptococcus and Corynebacterium species. Yeasts such as Saccharomyces cerevisiae, Geotrichum fermentum, Candida tropicalis, Rhodotorula graminis, and molds such as Aspergillus species, Fusarium subglutinans, Rhizopus nigricans, and Penicillum citrinum have been reported to be present at the early stage of the fermentation of these products  , “Ugba” from African oil beans (Pentaclethra macrophylla) , “Ogiri” from either of castor oil beans (Ricinus Communis), Melon seeds (Citrullus vulgaris) (Nigeria), Fluted pumpkin seeds  (Nigeria), or Bambara groundnut seeds (Vigna subterranean) (Nigeria), “Okpiye” from Prosopis Africana seeds (Prosopis africana), “Owoh” from African yam beans (Sphenostylis stenocarpa) (Nigeria) and “Siljo” from Faba beans (Vicia faba) flour mixed with safflower (Carthamus tinctorius) (Ethiopia). Fermented condiments are produced by the alkaline fermentation of legume seeds and are associated with abundance of Bacillus subtilis, Bacillus megaterium, Bacillus circulans, Bacillus licheniformis, and Bacillus coagulans45. Other bacteria identified previously included strains of Lactiplantibacillus plantarum, Lb. acidophilus, Lb. delbrueckii subsp. bulgaricus, Yarrowia lipolytica, Saccharomyces cerevisiae, Saccharomyces rouxii, Rhodotorula glutinis, Staphylococcus aureus, Escherichia coli, Streptococcus natalensis, Proteus species, Alcaligenes species, Micrococcus, Corynebacterium, Enterococcus faecium, and Pseudomonas species47.African fermented legume products are important sources of proteins, which are made more bioavailable following microbial digestion and detoxification, thereby contributing to countering malnutrition. These foods are often produced as condiments but have not been successfully commercialized on a large scale due to their short shelf-life and challenges with achieving packaging standards for the finished products and, so, continue to be produced using traditional methods48. Various researchers have documented the dominance of LABs in African fermented milk, including strains of Lb. delbrueckii subsp. bulgaricus, Lb. acidophilus, Lactococcus lactis subsp. lactis, Lactococcus lactis subsp. cremoris, Limosilactobacillus fermentum, Ligilactobacillus salivarius, Lactiplantibacillus plantarum, Enterococcus, and Streptococcus species , “Wara” , “Fene” , “Suusac” , “Pendidam” (Cameroon), “Gariss” (Sudan), “Nyamie” (Ghana), “Leben/lben” (Tunisia) and “Kulenaoto” (Kenya) are important fermented dairy products of considerable nutritional importance providing macronutrients, micronutrients, and health-promoting microorganisms. Such dairy fermented foods are normally introduced by backslopping of previously fermented dairy products to serve as a starter culture for a new fermentation. Fermented dairy products are widely consumed by Africans due to the health-promoting benefits linked to the microbial contents of the foods and the presence of specific micronutrientses Table 48–51. Ot53. African fermented foods have been reported to be sources of many potentially nutritionally beneficial and/or health-promoting components, including microorganisms. Unsurprisingly, from a microbial perspective, there has been a particular focus on members of the lactic acid bacteria such as members of the former genus Lactobacillus54 , Lactococcus species  Pediococcus species and Weissella species 56. We use the word “potentially” with care, as many indications of health benefits do not match the criteria required to use the term probiotic57. Further investigations are needed  before the term probiotic can be used in the context of these microorganisms.As noted above, there is a wide range of locally produced fermented foods and beverages across Africa. This results from a wide variety of fermentation types , substrates, microorganisms, and methods. Although food fermentation in Africa mostly occurs through spontaneous fermentation, there are also instances where the microorganisms were introduced by backslopping from previously fermented foods into new fermentations58. There are, however, limited reports on clinical randomized trials on the application of African fermented foods to improve general health with positive outcomes on conditions such as acute diarrhea in infants61. This could be associated with the microbiota in controlling enteric pathogens by inhibiting attachment of the pathogens’ pili to gut mucosa, through the release of organic acids and antimicrobial peptides, by competing for limited nutrients as well as the neutralization of exotoxins released by pathogens63.Despite this, the data that is available in relation to the health-promoting properties of specific African fermented foods and their associated microorganisms provides cause for optimism, with potential future applications including preventing the colonization of pathogens, treatment of infectious diseases, improved lactose tolerance, immune system modulations, improved barrier function, interference with quorum sensing signaling and the production of antimicrobial substancesClostridioides difficile infection64. However, from a practical “every day,” perspective, dietary modulation of the human microbiome is a more appealing alternative. Fermented foods were a particularly popular option in this regard, with the potential for modulation of the host microbiome (or host cell activity) by components of the foods . Studies showing consumption of fermented foods is both correlated with, and responsible for, improved health outcomes65. Despite this, fermented foods and associated microbes remain surprisingly relatively underexplored, particularly in respect to potential health benefits and modulation of the human microbiome66. African fermented foods are especially underexplored in this respect67. Fundamentally, African fermented food research provides researchers with novel insights into underexplored foods (and associated microbiota), but applying the research can also be beneficial for empowering local non-scientific producers of the food with prospects for commercialization and health-promoting benefits70.The gut microbiome is now increasingly becoming a target with a view to improving human health including, in its most extreme form, the use of fecal microbiome transplants for the treatment of 71. Microbial hydrolysis of certain prebiotics has also been reported to reduce anti-nutritional factors and thus improve the nutritional profile of the food products. Fermented cereal foods  made from maize were previously reported to be a very rich source of vitamin B complex including folic acid, pyridoxine, pantothenic acid, thiamine, and riboflavin while fermented sorghum-based foods  are rich in amino acids such as alanine, aspartic acid, glutamic acid, leucine, phenylalanine proline, and valine with low tryptophan and lysine. Some studies have also reported the presence of trace amounts of β-carotene73.The cereal-based African fermented foods  are rich in bioactive substances released during fermentation but also contain anti-nutritional factors including polyphenols, tannins, and phytic acid, which reduce the digestibility and bioavailability of carbohydrates and proteins. These foods can also serve as a source of prebiotics, which are “substrates that are selectively utilized by host microorganisms conferring health benefits”, which include non-digestible dietary fractions, such as oligosaccharides, polysaccharides, resistant starches, polyphenols, and resistant proteins74. Despite the increasingly widespread use of these approaches, the study of fermented foods in Africa still relies heavily on the use of classical culture-based methods combined with some polymerase chain reaction (PCR)-based techniques. Other population-based analyses such as those involving DGGE (denaturing gradient gel electrophoresis) have also been used in the past75 but do not facilitate the easy identification of the taxa present. In addition, the use of DGGE provides a dependable and considerable profile of diverse bacteria communities in cereal-based fermented foods (Amasi) commonly consumed in south Africa76 and PCR-DGGE fingerprints of microbial successional changes during fermentation of cereal-legume weaning foods in Nigeria77. Currently, Rep-PCR fingerprinting, 16S and 23S rRNA gene sequencing techniques, shotgun metagenomics sequencing, and whole-genome sequencing are infrequently used to study the microbial ecology of African fermented foods, and the microbial species/strains that are present therein. This infrequent use reflects the lack of access to the appropriate infrastructure and the high cost of applying HTS techniques to study the microbiomes and genomic diversity of strains in Africa fermented foods79.In recent years, high-throughput sequencing (HTS) techniques have been used to an increasing extent to study the microbiome of fermented foods from across the globe, including gaining insights into microbial dynamics, food safety, and putative health benefits83.Frequently, the methods used to analyze fermented foods in Africa precluded the characterization of non-culturable, or difficult to culture microbes, and thus specific components of microbial populations in these food ecosystems may have been overlooked. Although some recent studies have tried to address this problem by developing modified DNA extraction protocols for subsequent genomic and metagenomic sequencing of selected African fermented foods such as “Dengue” (Burkina-Faso), “Lafun” (Nigeria), “Ogi” (Nigeria), “Masau” (Zimbabwe), “Obusera” (Uganda), “Fura” (Ghana), the full potential of HTS has yet to be widely applied to the study of African fermented food microbiomes84. In Africa, whole-genome sequencing was initially used for the analysis and surveillance of foodborne pathogens. This process supplements other pre-existing methods such as culture-based isolation, bio-typing, serotyping, genotyping, as well as virulence and antimicrobial gene detection for food-associated pathogenic microorganisms86. As discussed, this technique could also be harnessed to assess food isolates from the perspective of their value as a starter or adjunct cultures by identifying genotypic traits underpinning features such as the production of bacteriocins or other antimicrobials of relevance from most consumed African fermented foods for improved food preservation and shelf-life quality as listed in Table Advances in microbial analysis of the food ecosystem have involved the use of HTS techniques such as whole-genome sequencing, amplicon bases meta-taxonomic approaches (such as 16S rRNA sequencing), shotgun metagenomics, and (meta) transcriptomics, and rely on downstream bioinformatics analysis87. In the latter case, the calculation of Average Nucleotide Identity scores of newly assembled MAGs with existing genome databases facilitate taxonomic assignment at species and even at strain level88.The 16S rRNA gene amplification combined HTS allows for genus level assessment of the microbiota and technological improvement is constantly reducing sequencing costs, resulting in affordability of HTS in diverse research applications. Shotgun metagenomic sequencing facilitates deeper insights into the microbiome, allowing strain-level identification, functional annotation including carbohydrate pathways and bioactive molecule production (such as bacteriocins), and the assembly of high-quality genomes in the form of metagenome-assembled genomes (MAGs)89. As DNA sequencing technologies continue to improve, the use of existing techniques inevitably becomes more cost-effective, facilitating the more widespread use of these techniques globally. Indeed, access to HTS-based analyses is slowly becoming more accessible to African researchers interested in fermented foods and, as noted, it is hoped that this can contribute greatly towards the development of nutrient-based and fortified African fermented food90. As previously noted, the emergences of these innovations provide many possibilities for future research relating to commonly consumed African fermented foods shown in Fig. 98. As noted above, combining long and short read data can facilitate the complete assembly of genomes including plasmids, mitigating some of the issues related to ambiguous assignment and false positives. However, sequencing-based analyses  need validation by culture-based experiments.Several HTS sequencing platforms, such as those developed by Illumina, Ion Torrent, PacBio, BGI, and Oxford Nanopore technologies and protocols could reveal microbial taxonomy, diversity, functional potential, and/or gene expression patterns with different results. The Illumina sequencing platforms have been widely employed for the generation of extremely high quality, short reads of up to 2×300 bp. Conversely, MinION platform based on single-molecule sequencing technology generates considerably longer read lengths but with lower accuracy and read numbers than short-read HTS platforms. Short read sequencing is not capable of completely closing bacterial genomes from mixed communities while using long-read data with a high error rate has other associated drawbacks, such as, for example, an inability to reliably identify single-nucleotide polymorphisms. However, by combining both approaches for whole-genome sequencing, fully assembled chromosomes and plasmids can be recovered with low error rates17. While fermented food products around the world are increasingly being studied using shotgun metagenomic techniques, very few studies have explored the use of shotgun sequencing in African fermented foods, with some exceptions being “Nunu” (Ghana), “Kokonte” (Ghana/Togo) and “Wagashi” (Benin)17. Leech et al. analyzed 58 fermented foods including “Nunu” (Ghana) and “Wagashi” (Benin) through shotgun metagenomic sequencing, which provided key insights into their microbial ecology and functional capabilities for health-promoting attributes18. Diaz et al. also investigated 40 African fermented foods using 16S rRNA metagenomic sequencing and found members of the genera Lactobacillus, Leuconostoc, Weissella, Bacillus, Zygomonas, Streptococcus, Zymomonas, Gemmella, Brevibacillus, Jeotgalicoccus, Vagococcus, Atopstipes, and others belonging to the family Carnobacteriaceae79. While these studies have highlighted the merits of applying HTS technologies to the study of African fermented food communities, there is a need for a broader-scale application.For community-based investigations, amplicon sequencing of the hypervariable (V1–V4 or V4–V5) regions of the 16S rRNA amplicon gene and shotgun metagenomics, sequencing have improved insights into African fermented food productsFermented foods are a key component of the African diet and the associated microbes and microbial communities are being studied in ever-greater depth. The further harnessing of traditional culture-based and HTS techniques has the potential to characterize and optimize the microbiome of African fermented foods, from commercial to nutritional and health-promoting aspects. HTS can also be used in isolation, or in combination with cultivation, to identify virulence, antimicrobial resistance determinants, and provide genotypic characterization. Further access to HTS platforms and the democratization of sequencing have the potential to be of considerable value with respect to the further development of these foods.