Characterising shoot and root system trait variability and contribution to genotypic variability in juvenile cassava (Manihot esculenta Crantz) plants

Genome-wide association studies identifies genetic loci related to fatty acid and branched-chain amino acid metabolism and histone modifications under varying nitrogen treatments in safflower (Carthamus tinctorius)

Effective identification and usage of genetic variation are prerequisites for developing nutrient-efficient cultivars. A collection of 94 safflower (Carthamus tinctorius ) genotypes (G) was investigated for important morphological and photosynthetic traits at four nitrogen (N) treatments. We found significant variation for all the studied traits except chlorophyll b (chl b ) among safflower genotypes, nitrogen treatments and G×N interaction. The examined traits showed a 2.82-50.00% increase in response to N application. Biological yield (BY) reflected a significantly positive correlation with fresh shoot weight (FSW), root length (RL), fresh root weight (FRW) and number of leaves (NOL), while a significantly positive correlation was also observed among carotenoids (C), chlorophyll a (chl a ), chl b and total chlorophyll content (CT) under all treatments. Superior genotypes with respect to plant height (PH), FSW, NOL, RL, FRW and BY were clustered into Group 3, while genotypes with better mean performance regarding chl a , chl b C and CT were clustered into Group 2 as observed in principal component analysis. The identified eight best-performing genotypes could be useful to develop improved nitrogen efficient cultivars. Genome-wide association analysis resulted in 32 marker-trait associations (MTAs) under four treatments. Markers namely DArT-45481731 , DArT-17812864 , DArT-15670279 and DArT-45482737 were found consistent. Protein-protein interaction networks of loci associated with MTAs were related to fatty acid and branched-chain amino acid metabolism and histone modifications.

Temporal phenotypic variation of spinach root traits and its relation to shoot performance

The root system is important for the growth and development of spinach. To reveal the temporal variability of the spinach root system, root traits of 40 spinach accessions were measured at three imaging times (20, 30, and 43 days after transplanting) in this study using a non-destructive and non-invasive root analysis system. Results showed that five root traits were reliably measured by this system (RootViz FS), and two of which were highly correlated with manually measured traits. Root traits had higher variations than shoot traits among spinach accessions, and the trait of mean growth rate of total root length had the largest coefficients of variation across the three imaging times. During the early stage, only tap root length was weakly correlated with shoot traits (plant height, leaf width, and object area (equivalent to plant surface area)), whereas in the third imaging, root fresh weight, total root length, and root area were strongly correlated with shoot biomass-related traits. Five root traits (total root length, tap root length, total root area, root tissue density, and maximal root width) showed high variations with coefficients of variation values (CV ≥ 0.3, except maximal root width) and high heritability (H2 > 0.6) among the three stages. The 40 spinach accessions were classified into five subgroups with different growth dynamics of the primary and lateral roots by cluster analysis. Our results demonstrated the potential of in-situ phenotyping to assess dynamic root growth in spinach and provide new perspectives for biomass breeding based on root system ideotypes.

Rapid and low-cost screening for single and combined effects of drought and heat stress on the morpho-physiological traits of African eggplant (Solanum aethiopicum) germplasm

Drought and heat are two stresses that often occur together and may pose significant risks to crops in future climates. However, the combined effects of these two stressors have received less attention than single-stressor investigations. This study used a rapid and straightforward phenotyping method to quantify the variation in 128 African eggplant genotype responses to drought, heat, and the combined effects of heat and drought at the seedling stage. The study found that the morphophysiological traits varied significantly among the 128 eggplants, highlighting variation in response to abiotic stresses. Broad-sense heritability was high (> 0.60) for chlorophyll content, plant biomass and performance index, electrolyte leakage, and total leaf area. Positive and significant relationships existed between biomass and photosynthetic parameters, but a negative association existed between electrolyte leakage and morpho-physiological traits. The plants underwent more significant stress when drought and heat stress were imposed concurrently than under single stresses, with the impact of drought on the plants being more detrimental than heat. There were antagonistic effects on the morphophysiology of the eggplants when heat and drought stress were applied together. Resilient genotypes such as RV100503, RV100501, JAMBA, LOC3, RV100164, RV100169, LOC 3, RV100483, GH5155, RV100430, GH1087, GH1087*, RV100388, RV100387, RV100391 maintained high relative water content, low electrolyte leakage, high Fv/Fm ratio and performance index, and increased biomass production under abiotic stress conditions. The antagonistic interactions between heat and drought observed here may be retained or enhanced during several stress combinations typical of plants' environments and must be factored into efforts to develop climate change-resilient crops. This paper demonstrates improvised climate chambers for high throughput, reliable, rapid, and cost-effective screening for heat and drought and combined stress tolerance in plants.

On-Farm Multi-Environment Evaluation of Selected Cassava (Manihot esculenta Crantz) Cultivars in South Africa

Cassava is an important starchy root crop grown globally in tropical and subtropical regions. The ability of cassava to withstand difficult growing conditions and long-term storability underground makes it a resilient crop, contributing to food and nutrient security. This study was conducted to evaluate the performance and adaptability of exotic cassava cultivars across different environments in South Africa and to recommend genotypes for cultivation. A total of 11 cassava cultivars were evaluated at six on-farm sites, using a randomized complete block design with three replications. There were highly significant (p < 0.001) variations between genotypes, environments, and their interaction for all yield and yield-related traits studied. This indicates the need to test the genotypes in multiple environments before effective selection and commercialization can be undertaken. MSAF2 and UKF4 showed the overall best performances for most of the traits, whilst UKF9 (49.5%) and P1/19 (48.5%) had the highest dry matter yield. UKF4 (102.7 t ha−1) had the highest yield and greatest root yield stability across environments. MSAF2 did not perform consistently across environments because it was highly susceptible to cassava mosaic disease (CMD). MSAF2 could be used as a donor parent to generate novel clones with large numbers of marketable roots, and high fresh root yields, if the other parent can provide effective resistance to CMD. Based on genotype and environmental mean, Mabuyeni (KwaZulu-Natal), Mandlakazi (Limpopo), and Shatale (Mpumalanga) were found to be better environments for cassava cultivation and testing. This study is a pioneer in cassava research using multiple environments in South Africa. It provides baseline information on the performance of currently available cassava clones, their adaptation to multiple sites, the identification of suitable test sites, and information on current genetic resources for a future breeding program.

Cassava root crown phenotyping using three-dimension (3D) multi-view stereo reconstruction

Phenotypic analysis of cassava root crowns (CRCs) so far has been limited to visual inspection and very few measurements due to its laborious process in the field. Here, we developed a platform for acquiring 3D CRC models using close-range photogrammetry for phenotypic analysis. The state of the art is a low cost and easy to set up 3D acquisition requiring only a background sheet, a reference object and a camera, compatible with field experiments in remote areas. We tested different software with CRC samples, and Agisoft and Blender were the most suitable software for generating high-quality 3D models and data analysis, respectively. We optimized the workflow by testing different numbers of images for 3D reconstruction and found that a minimum of 25 images per CRC can provide high quality 3D models. Up to ten traits, including 3D crown volumes, 3D crown surface, root density, surface-to-volume ratio, root numbers, root angle, crown diameter, cylinder soil volume, CRC compactness and root length can be extracted providing novel parameters for studying cassava storage roots. We applied this platform to partial-inbred cassava populations and demonstrated that our platform provides reliable 3D CRC modelling for phenotypic analysis, analysis of genetic variances and supporting breeding selection.

Genetic diversity of local and introduced cassava germplasm in Burundi using DArTseq molecular analyses

In Burundi most small-scale farmers still grow traditional cassava landraces that are adapted to local conditions and have been selected for consumer preferred attributes. They tend to be susceptible, in varying degrees, to devastating cassava viral diseases such as Cassava Brown Streak Disease (CBSD) and Cassava Mosaic Disease (CMD) with annual production losses of US$1 billion. For long term resistance to the disease, several breeding strategies have been proposed. A sound basis for a breeding program is to understand the genetic diversity of both landraces and elite introduced breeding cultivars. This will also assist in efforts to conserve landraces ahead of the broad distribution of improved varieties which have the possibility of replacing landraces. Our study aimed at determining the genetic diversity and relationships within and between local landraces and introduced elite germplasm using morphological and single nucleotide polymorphism (SNP) markers. A total of 118 cultivars were characterized for morphological trait variation based on leaf, stem and root traits, and genetic variation using SNP markers. Results of morphological characterization based on Ward’s Method revealed three main clusters and five accessions sharing similar characteristics. Molecular characterization identified over 18,000 SNPs and six main clusters and three pairs of duplicates which should be pooled together as one cultivar to avoid redundancy. Results of population genetic analysis showed low genetic distance between populations and between local landraces and elite germplasm. Accessions that shared similar morphological traits were divergent at the molecular level indicating that clustering using morphological traits was inconsistent. Despite the variabilities found within the collection, it was observed that cassava germplasm in Burundi have a narrow genetic base.

Causal shoot and root system traits to variability and plasticity in juvenile cassava (Manihot esculenta Crantz) plants in response to reduced soil moisture

Cassava is an important source of food security and livelihoods for millions of consumers daily. Water deficit conditions are one of the major factors that affect the development of root system architecture (RSA) and consequently, crop productivity, and yet, due to its long maturity periods and bulky storage root systems, RSA studies in cassava are uncommon. The objective of this study was to identify traits that are responsible for the variability and plastic responses of cassava in response to drought at the juvenile stage of growth. Eight cassava genotypes were grown in soil-filled pots under well-watered and droughted conditions for up to 45 days and multivariate analyses employed to determine the major contributory traits to variability and the relative distance plasticity index (RDPI) was computed to evaluate plasticity. There were significant genotypic variations for most of the traits measured. Drought generally inhibited root production and development and the degree of inhibition was between 2 and 22%. Regardless of the soil moisture condition, traits which differentiated the RSA included root biomass, root numbers, root branching density, and total root length, and these were also the important contributory traits to variability under well-watered soil conditions. Important contributory traits to variability traits under drought were shoot-related traits such as leaf area and shoot biomass, and also root system traits such as nodal root number, root biomass, diameter and branching density. Phenotypic plasticity was found in most traits where the number, branching density and diameter of upper nodal roots presented the highest RDPI. These traits corresponded with the traits contributing greatly to variation. Plastic responses of cassava to drought were dependent on trait and genotype. It is concluded that upper nodal roots-related traits could have importance in breeding cassava to better tolerate water deficit conditions. The secondary growth and ability to maintain or increase the upper nodal root count or density under limited soil moisture may be related to good growth and yield performance of cassava under drought conditions. Upper nodal roots could be used to screen and select cassava genotypes adapted to drought at the juvenile stage but as a potential indirect selection strategy, the persistence and pertinence of these traits and their relationship with yield and yield components under drought conditions in the field must be confirmed.

Phenotypic Characterization of Tiger Nuts (Cyperus esculentus L.) from Major Growing Areas in Ghana

Tigernut (Cyperus esculentus Lativum) is an important but understudied and underutilized crop in Ghana. The tubers are highly appreciated for their health benefits and nutritive value. To contribute to the conservation process of tiger nut and identify elite genotypes, this study was conducted to assess phenotypic variability in tiger nut genotypes in Ghana. Sixty-four (64) genotypes were collected from major tiger nut growing areas in Ghana. The genotypes were field-grown and characterized based on phenotypic and yield traits. Similarity coefficient (Bray-Curtis) was between 0.82 and 0.98, indicating low variability in both qualitative and quantitative characters. The cophenetic correlation coefficient was 0.64. The genotypes were mainly brown with only a few black (6) tubers from the central region. Materials collected from parts of the eastern region (Aduamoah) generally recorded the highest tuber weight. Tuber weight depended on plant height and number of tillers. There were high tillering genetic materials among the genotypes. Tubers were categorised as oval (10), ovoid (33), or oblong (15). The genotypes clustered into two groups based on shoot and tuber characteristics, rather than on geographical origin. The low genetic diversity among the genotypes suggested either the possible existence of a network among tiger nut farmers in Ghana in circulating the planting material or some form of seed sorting for uniform and homogenous planting materials has been carried out over the years. Our results point to the imperativeness to expand the genetic base of the tiger nuts to facilitate its improvement in Ghana.

The search for yield predictors for mature field-grown plants from juvenile pot-grown cassava (Manihot esculenta Crantz)

Cassava is the 6th most important source of dietary energy in the world but its root system architecture (RSA) had seldom been quantified. Ability to select superior genotypes at juvenile stages can significantly reduce the cost and time for breeding to bridge the large yield gap. This study adopted a simple approach to phenotyping RSA traits of juvenile and mature cassava plants to identify genotypic differences and the relationships between juvenile traits and harvest index of mature plants. Root classes were categorised and root and shoot traits of eight (8) juvenile pot-grown cassava genotypes, were measured at 30 and 45 days after planting (DAP). The same or related traits were measured at 7 months after planting of the same genotypes grown in the field while yield and yield components were measured in 12-months old field-grown plants. The field experiment was done in 2017 and repeated in 2018. Differences between genotypes for the measured traits were explored using analysis of variance (ANOVA) while traits in juvenile plants were correlated or regressed onto traits measured in 7- and 12-months old plants. The results show significant genotypic variations for most of the traits measured in both juvenile and 7-months old plants. In the 12-months old plants, differences between genotypes were consistent for both 2017 and 2018. Broad-sense heritability was highest for the number of commercial roots (0.87) and shoot fresh weight (0.78) and intermediate for the total number of roots (0.60), harvest index (0.58), fresh weight of roots (0.45). For all the sampling time points or growth stages, there were greater correlations between traits measured at a particular growth stage than between the same traits at different growth stages. However, some juvenile-mature plant trait relationships were significant, positive and consistent for both 2017 and 2018. For example, total root length and the total number of roots in 30 DAP, and branching density of upper nodal roots in 45 DAP, positively correlated with harvest index of 12-months old plants in both 2017 and 2018. Similarly, the diameter of nodal roots, for example, had a negative, significant correlation with fresh shoot biomass of mature plants in both 2017 and 2018. Regression of traits measured in 30 DAP explained up to 22% and 36% of the variation in HI of mature plants in 2017 and 2018, respectively. It is concluded that the simple, rapid, inexpensive phenotyping approach adopted in this study is robust for identifying genotypic variations in juvenile cassava using root system traits. Also, the results provide seminal evidence for the existence of useful relationships between traits of juvenile and mature cassava plants that can be explored to predict yield and yield components.

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task.

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task.

Phenotypic variation of cassava root traits and their responses to drought

PREMISE OF THE STUDY

The key to increased cassava production is balancing the trade-off between marketable roots and traits that drive nutrient and water uptake. However, only a small number of protocols have been developed for cassava roots. Here, we introduce a set of new variables and methods to phenotype cassava roots and enhance breeding pipelines.

METHODS

Different cassava genotypes were planted in pot and field conditions under well-watered and drought treatments. We developed cassava shovelomics and used digital imaging of root traits (DIRT) to evaluate geometrical root traits in addition to common traits (e.g., length, number).

RESULTS

Cassava shovelomics and DIRT were successfully implemented to extract root phenotypes, and a large phenotypic variation for root traits was observed. Significant correlations were found among root traits measured manually and by DIRT. Drought significantly decreased shoot dry weight, total root number, and root length by 84%, 30%, and 25%, respectively. High adventitious root number was associated with increased shoot dry weight (r = 0.44) under drought.

DISCUSSION

Our methods allow for high-throughput cassava root phenotyping, which makes a breeding program targeting root traits feasible. We suggest that root number is a breeding target for improved cassava production under drought.

Root Branching and Nutrient Efficiency: Status and Way Forward in Root and Tuber Crops

Plants are immobile organisms that require roots to efficiently and cost-effectively exploit their habitat for water and nutrients. Plant root systems are dynamic structures capable of altering root branching, root angle, and root growth rates determining overall architecture. This plasticity involves belowground plant-root mediated synergies coupled through a continuum of environmental interactions and endogenous developmental processes facilitating plants to adapt to favorable or adverse soil conditions. Plant root branching is paramount to ensure adequate access to soil water and nutrients. Although substantial resources have been devoted toward this goal, significant knowledge gaps exist. In well-studied systems such as rice and maize, it has become evident that root branching plays a significant role in the acquisition of nutrients and other soil-based resources. In these crop species, specific root branching traits that confer enhanced nutrient acquisition are well-characterized and are already being incorporated into breeding populations. In contrast, the understanding of root branching in root and tuber crop productivity has lagged behind. In this review article, we highlight what is known about root branching in root and tuber crops (RTCs) and mark new research directions, such as the use novel phenotyping methods, examining the changes in root morphology and anatomy under nutrient stress, and germplasm screening with enhanced root architecture for more efficient nutrient capture. These directions will permit a better understanding of the interaction between root branching and nutrient acquisition in these globally important crop species.

Systematic review of the effects of agricultural interventions on food security in northern Ghana

Background

Food insecurity and poverty rates in Ghana are highest in the districts from latitude 8° N upwards. These have motivated several interventions aimed at addressing the food insecurity via promoting agricultural growth. An assessment of the overall impact of these interventions on food security is necessary to guide policy design and future interventions.

Methods and findings

A systematic review was used to assess the cumulative evidence of the effect of development interventions, implemented from 2006 to 2016 on food security, especially in Northern Ghana. Information were retrieved from over 20 Government and non-Governmental organisations through online search and actual visits. The number of studies included in systematic review was 22. The study showed that a large number of interventions have been implemented in Northern Ghana over the study period. Access to quality extension services, training and capacity building was a major intervention strategy. About 82% of studies considered increasing production but only 14% of the studies reported on changes in yield. About 42% of the included studies used market access as a strategy but about 44% reported increase in incomes of beneficiaries (with only seven studies providing numerical evidence for this claim). The ranking of frequency of intervention strategies was in the order extension and capacity building > production > postharvest value addition > water and irrigation facilities > storage facilities > input supply. A substantial number of the studies had no counterfactuals, weakening confidence in attributing impacts on food security for even the beneficiaries.

Conclusions

It is concluded that evidence for impacts of the interventions on food security was weak, or largely assumed. A logical recommendation is the need for development partners to synchronise their measurement and indicators of food security outcomes. It is also recommended that some food security indicators are explicitly incorporated into intervention design while bearing in mind the potential need for counterfactuals.