The rational application of fertilizers is crucial for achieving high crop yields and ensuring global food security. The use of biopolymers for slow-release fertilizers (SRFs) development has emerged as a game-changer and environmentally sustainable pathway to enhance crop yields by optimizing plant growth phases. Herein, with a renewed focus on circular bioeconomy, a novel functionalized lignin-based coating material (FLGe) was developed for the sustained release of nutrients. This innovative approach involved the extraction and sustainable functionalization of lignin through a solvent-free esterification reaction with humic acid─an organic compound widely recognized for its biostimulant properties in agriculture. The primary objective was to fortify the hydration barrier of lignin by reducing the number of its free hydroxyl groups, thereby enhancing release control, while simultaneously harnessing the agronomic benefits offered by humic acid. After confirming the synthesis of functionalized lignin (FLGe) through 13C NMR analysis, it was integrated at varying proportions into either a cellulosic or starch matrix. This resulted in the creation of five distinct formulations, which were then utilized as coatings for diammonium phosphate (DAP) fertilizer. Experimental findings revealed an improved morphology and hardness (almost 3-fold) of DAP fertilizer granules after coating along with a positive impact on the soil's water retention capacity (7%). Nutrient leaching in soil was monitored for 100 days and a substantial reduction of nutrients leaching up to 80% was successfully achieved using coated DAP fertilizer. Furthermore, to get a fuller picture of their efficiency, a pot trial was performed using two different soil textures and demonstrated that the application of FLGe-based SRFs significantly enhanced the physiological and agronomic parameters of wheat, including leaf evolution and root architecture, resulting in an almost 50% increase in grain yield and improved quality. The results proved the potential of lignin functionalization to advance agricultural sustainability and foster a robust bioeconomy aligning with the premise "from the soil to the soil".

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Due to the adverse effect of prolonged drought stress on plants, accurate detection is essential for water use efficiency and maintaining productivity. Hyperspectral imaging is frequently used for non-invasive plant phenotyping, allowing for the long-term monitoring of crop health due to its sensitivity to subtle changes in leaf constituents. The broad spectrum of hyperspectral data enables the development of multiple vegetation indices (Vis) derived from the different spectral regions to estimate plant biophysical and biochemical traits. However, the known VIs often do not generalize well and perform poorly for multiple plant stresses. This study proposes new VIs combined with machine learning models to identify drought stress in wheat species under different nitrogen (N) levels. A wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high N and drought-stress low-N. In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) were taken regularly, serving as the ground truth data for this study. Sensitive spectral features were selected using a custom-designed ensemble modelling technique. New drought VIs are proposed using different combinations of the selected features. Three classification models (support vector machines, random forest and deep neural network) were developed and trained using four sets of data: known VIs, proposed VIs, combined VIs (from the known and proposed VIs) and PCA-transformed features (over the whole spectral data). From the results, the proposed VIs outperformed the known VIs, yielding > 0.94 accuracies for all three models, and the performance improved when they were trained with the combined VIs. The combined VIs were used to train three regression models to predict the stomatal conductances and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat.

The underperformance of agricultural education systems in Africa is evident through various indicators such as increased unemployment among recent agricultural graduates, inefficiencies in agricultural product value chains, and a decline in enrollment in agricultural schools. The AgriENGAGE project, which included eight African universities, was supported through the Erasmus+ Capacity Building in Higher Education program funded by the European Commission to address these challenges. The project aimed to contribute toward revitalizing agricultural education systems to stimulate agricultural transformation and enhance the sector’s competitiveness while meeting the labor market’s demands. This article draws on lessons learned at eight universities to provide a perspective on agri-entrepreneurship education integration in African universities. We provide descriptions, experiences, and insights on agri-entrepreneurship education integration in partner universities in Kenya, Benin, Morocco, and Uganda. Based on these experiences, we provide perspectives on reducing youth unemployment and improving the effectiveness of agricultural education in contributing to the development of sustainable food systems.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green–red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

Proximal sensing has been used extensively for decades to assess crop nitrogen (N) status using either a hand-held chlorophyll meter or vegetation indices such as the normalized difference vegetation index (NDVI) for various crops.However, little has been done on quinoa (Chenopodium quinoa Willd.).In this study, we investigated how the SPAD chlorophyll meter values and NDVI could be used as indicators for N status and how they can be linked to quinoa performance in terms of photosynthesis and yield.The objectives of this study were to: 1) evaluate SPAD values and NDVI as indicators of N status, 2) assess their relevance over the crop cycle, and 3) investigate their link to the performance in terms of net CO2 assimilation and grain yield at harvest.A pot experiment based on varying nitrogen and phosphorus (P) input conditions was conducted in the glasshouse at Cranfield University, UK.The results showed that both SPAD and NDVI correlated similarly with the leaf N content (%) (R 2 =0.76,R 2 =0.82, p<0.001, respectively).High correlations between SPAD and NDVI were also observed at 58 DAS (R 2 =0.67) and across the entire crop cycle (R 2 =0.84), validating the utility of both parameters for N status monitoring.Furthermore, significant differences between treatments were observed at different growth stages when SPAD and NDVI were measured across the crop cycle.Strong significant correlations between SPAD and NDVI with the net CO2 assimilation (Anet) (R 2 =0.86,R 2 =0.81, p<0.001, respectively) were recorded.SPAD values and NDVI significantly correlated with grain yield at harvest (R 2 =0.68,R 2 =0.80, p<0.001, respectively).While SPAD and NDVI are potentially useful tools to improve N fertilizer management and develop in-season yield predictions in quinoa at relatively low-cost, alternative non-saturating spectral indices need to be explored to improve accuracy.

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Seeds represent the most vulnerable and critical stages of a plant's life, making them susceptible to several factors like genotype, environment, and seed quality, affecting germination, emergence, and vigor. Seed coating provides a (1) protective layer that improves plant early-stages development through nutrients, (2) plant growth-promoting microbes, and (3) biocontrol agents. In addition, its uniform application and precise supply contribute to making it a cost-effective technique for product delivery, germination homogeneity, seedling establishment, and preventing deficiencies in later growth stages. As only the needed amount of fertilization is provided to the seed, seed coating is considered a targeted application method for microorganism's supply and a cost-effective and sustainable one for macro- and micronutrient delivery. Within this context, beneficial bacteria are of primary importance in alleviating environmental stresses and enabling a specific resistance to the plant. Bilateral interaction is the core of the synergetic relationship between a plant and its associated microbiome and helps shape this latter and modulate it for optimal performance. However, when it comes to using plant growth-promoting bacteria (PGPB), combined with an efficient seed coating technology, the challenge lies in the viability of the bacteria under this protective layer. Therefore, the actual market needs more adaption for both seed coating material and bacterial inoculum despite the rising demand. Thus, the present chapter aims to discuss the opportunities and challenges of microbial seed coating technology and the mechanisms behind seed bacterial treatments.

To obviate adverse effects from the non-biodegradability of certain polymer-based slow-release fertilizers (SRFs) and to offset higher operational costs, the use of biopolymers as coating material has recently caught interest in the research circles. The present work aims to design a sustainable coating material based on biodegradable polymers. To this end, Alfa plant was initially exploited as a viable sustainable source for the extraction of lignin (LGe), which was in turn integrated into the development of a three-dimensional cross-linked network, including methylcellulose (MC) as a matrix and citric acid (CA) as a cross-linking agent. Then, the designed coating material was applied onto Di-ammonium Phosphate (DAP) and Triple Superphosphate (TSP) water-soluble fertilizers in a rotating pan machine. Chemical, physical, and biodegradation studies have confirmed that the coating material is environmentally-friendly. Nutrients release experiments in water as well as in soil environments have proved the effectiveness of the MC and MC/LGe coating layers in delaying the nutrients discharge. Besides, the nutrients release from coated DAP and TSP lasted longer than 30 days. Furthermore, the coating film enhanced the fertilizers mechanical resistance and boosted the soil water retention capacity. The agronomic evaluation has also confirmed their remarkable potential in enhancing wheat leaf area, chlorophyll content and biomass, in addition to the roots architecture and the final fruiting efficiency. These results showed that this hybrid composite could be used as an efficient coating material to produce slow-release fertilizers with multifunctional performances.

The pseudocereal grain, Quinoa (Chenopodium quinoa Willd.), has a great nutritional value due to its high contents of proteins, fiber, minerals, and vitamins. However, saponins naturally present outside the grains represent an obstacle to their consumption as human food. Before consumption, the grains are subjected to various treatments, which alter their nutritional value. In an attempt to eliminate the maximum of saponins using the wet process, while minimizing the washing conditions and preserving the nutritional quality, we explored the effects of several parameters, including volume of water, treatment time, soaking time, number of washing, and water temperature, followed by an optimization process using Box–Behnken Design, and finally, the impact of this process on the physicochemical and techno-functional properties of six quinoa genotypes seeds was evaluated. As a result, the variation of the treatment time, volume, and temperature of the water positively affected the saponins leaching. According to the quadratic model, the maximum percentages of eliminated saponins (96.53%−96.77%) were found at a temperature of 50°C, treatment times from 60 to 69 min, and water volumes from 6.99 to 7.50 mL per gram of seeds. The optimized method did not affect the proteins and microelements content (Zn, Mn, B, Mo), while a slight decrease of macro-elements (K, P, Ca, S, Mg) was noted in the level of some genotypes. On the other hand, a significant improvement of the techno-functional properties such as water and oil holding capacity was noted, with a sharp drop-in emulsifying activity in all genotypes without affecting the standard values of pH (6.4–6.8) and moisture content (10%−11%) of the seeds. Hence, the optimized method showed to be a more potential method for saponins removal than the currently used dry method.

Seed priming has been for a long time an efficient application method of biofertilizers and biocontrol agents. Due to the quick degradation of the priming agents, this technique has been limited to specific immediate uses. With the increase of awareness of the importance of sustainable use of biofertilizers, seed coating has presented a competitive advantage regarding its ability to adhere easily to the seed, preserve the inoculant, and decompose in the soil. This study compared primed Phaseolus vulgaris seeds with Rhizobium tropici and trehalose with coated seeds using a silk solution mixed with R. tropici and trehalose. We represented the effect of priming and seed coating on seed germination and the development of seedlings by evaluating physiological and morphological parameters under different salinity levels (0, 20, 50, and 75 mM). Results showed that germination and morphological parameters have been significantly enhanced by applying R. tropici and trehalose. Seedlings of coated seeds show higher root density than the freshly primed seeds and the control. The physiological response has been evaluated through the stomatal conductance, the chlorophyll content, and the total phenolic compounds. The stability of these physiological traits indicated the role of trehalose in the protection of the photosystems of the plant under low and medium salinity levels. R. tropici and trehalose helped the plant mitigate the negative impact of salt stress on all traits. These findings represent an essential contribution to our understanding of stress responses in coated and primed seeds. This knowledge is essential to the design of coating materials optimized for stressed environments. However, further progress in this area of research must anticipate the development of coatings adapted to different stresses using micro and macro elements, bacteria, and fungi with a significant focus on biopolymers for sustainable agriculture and soil microbiome preservation.

Agriculture has many challenges such as climate changes, droughts, and salinities. Which require urgent interventions to fast adaptation and diversification. In this context, the introduction of crops with a high value and a high tolerance to stress such as quinoa would enable to address simultaneously two interrelated challenges: abiotic stresses that undermine crop production and poverty that impacts the rural poorness. The quinoa Rehamna project funded by IDRC and implemented by ICBA and UM6P University in Morocco aims to develop the quinoa value chain toward achieving food and nutritional security of rural poor communities. The obtained results from the farmer’s survey show that 19b% of surveyed farmers already grow quinoa and 2/3 never heard about quinoa, among them 95% are interested to grow quinoa. The marketing study reveals that the psychological price that satisfies most of the consumers is ranging between 2 and 2.6 USD/500 gr of processed quinoa grains. The findings indicate that quinoa has great potential for both producers and consumers in Morocco and can be a judicious solution toward achieving food and nutritional security.

In semi-arid regions, water stress during seed germination and early seedling growth is the highest cause of crop loss. In nature, some seeds (for example, chia and basil) produce a mucilage-based hydrogel that creates a germination-promoting microenvironment by retaining water, regulating nutrient entry and facilitating interactions with beneficial microorganisms. Inspired by this strategy, a two-layered biopolymer-based seed coating has been developed to increase germination and water-stress tolerance in semi-arid, sandy soils. Seeds are coated with a silk/trehalose inner layer containing rhizobacteria and a pectin/carboxymethylcellulose outer layer that reswells upon sowing and acts as a water jacket. Using Phaseolus vulgaris (common bean) cultured under water-stress conditions in an experimental farm in Ben Guerir, Morocco, the proposed seed coating effectively delivered rhizobacteria to form root nodules, resulted in plants with better health and mitigated water stress in drought-prone marginal lands. A programmable seed coating technology has the potential to increase seed germination and water-stress tolerance in semi-arid, sandy soils.

New technologies that enhance soil biodiversity and minimize the use of scarce resources while boosting crop production are highly sought to mitigate the increasing threats that climate change, population growth, and desertification pose on the food infrastructure. In particular, solutions based on plant-growth-promoting bacteria (PGPB) bring merits of self-replication, low environmental impact, tolerance to biotic and abiotic stressors, and reduction of inputs, such as fertilizers. However, challenges in facilitating PGPB delivery in the soil still persist and include survival to desiccation, precise delivery, programmable resuscitation, competition with the indigenous rhizosphere, and soil structure. These factors play a critical role in microbial root association and development of a beneficial plant microbiome. Engineering the seed microenvironment with protein and polysaccharides is one proposed way to deliver PGPB precisely and effectively in the seed spermosphere. In this review, we will cover new advancements in the precise and scalable delivery of microbial inoculants, also highlighting the latest development of multifunctional rhizobacteria solutions that have beneficial impact on not only legumes but also cereals. To conclude, we will discuss the role that legislators and policymakers play in promoting the adoption of new technologies that can enhance the sustainability of crop production.

Agriculture is facing many challenges such as climate change, drought, and salinity. This calls for urgent interventions to induce fast adaptation and diversification. In this context, the introduction of crops with a high value and a high tolerance to stress such as quinoa would help address two interrelated challenges simultaneously: abiotic stresses that undermine crop production and poverty that impacts the rural poor. The quinoa Rehamna project funded by IDRC and implemented by ICBA and UM6P University in Morocco aims to develop the quinoa value chain toward achieving food and nutritional security of poor rural communities. Several high-yielding quinoa cultivars developed in ICBA have been introduced. Obtained results from the experimental station and on-farm trials show clearly that ICBA cultivars recorded higher yields than local cultivated seeds. Irrigation had a great effect on quinoa productivity as it was increasing for most cultivars by more than three times compared to rainfed conditions. Irrigation along with organic amendment increased yield four times for most of the tested cultivars. Production cost analysis showed that using mechanized production and processing practices combined with irrigation and organic amendment can reduce the cost of quinoa seed production and processing from 2.8 to 1.1 USD/kg compared to manual production system under rainfed conditions. It was concluded that to be more competitive, using mechanized production and processing along with irrigation, organic amendment, and high-yielding cultivars is the key solution to increase productivity, reduce cost and thus reduce the market price.

Saponins are an important group found in Chenopodium quinoa. They represent an obstacle for the use of quinoa as food for humans and animal feeds because of their bitter taste and toxic effects, which necessitates their elimination. Several saponins elimination methods have been examined to leach the saponins from the quinoa seeds; the wet technique remains the most used at both laboratory and industrial levels. Dry methods (heat treatment, extrusion, roasting, or mechanical abrasion) and genetic methods have also been evaluated. The extraction of quinoa saponins can be carried out by several methods; conventional technologies such as maceration and Soxhlet are the most utilized methods. However, recent research has focused on technologies to improve the efficiency of extraction. At least 40 saponin structures from quinoa have been isolated in the past 30 years, the derived molecular entities essentially being phytolaccagenic, oleanolic and serjanic acids, hederagenin, 3β,23,30 trihydroxy olean-12-en-28-oic acid, 3β-hydroxy-27-oxo-olean-12en-28-oic acid, and 3β,23,30 trihydroxy olean-12-en-28-oic acid. These metabolites exhibit a wide range of biological activities, such as molluscicidal, antifungal, anti-inflammatory, hemolytic, and cytotoxic properties.

Quinoa (Chenopodium quinoaWilld.) is a grain of great nutritional interest that gained international importance during the last decade. Before its consumption, this grain goes through many processes that can alter its nutritional value. Here we report the effect of processing (polishing and milling) and cooking (boiling and steaming) on the saponin content, mineral profile of 14 elements using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), protein content, and total phenolic compound. The polishing caused an average drop in the saponin content from 1.7% to 0.46% but induced important losses in mineral content (K, Mg, Ca, Zn, Co, Cu, Fe, Mn, and Ni), and phenolic compounds. However, the greatest nutritional degradation happened after milling due to the elimination of seed teguments and embryos, where over 50% of many minerals, 60% of protein content, and almost the totality of phenolic compounds, were lost. Cooking effect was less important than processing, but some significant losses were attested. Boiling caused a loss of up to 40% for some minerals like K, B, and Mo because of their hydrosolubility, and 88% of the polyphenols, while steaming allowed a better retention of those nutrients. Consuming polished quinoa instead of semolina and using steaming instead of boiling are trade-offs consumer needs to make to get optimal benefits from quinoa virtues.

In the present study, seventy-nine accessions, including two local wild species (Chenopodium album and C. murale) and several cultivated quinoa lines developed through recurrent selection in Morocco were screened for their resistance against Peronospora farinosa that is the causal agent of downy mildew disease. The method of artificial inoculation on detached leaves taken from the middle stage of the plant before the natural infection was used. Screened accessions showed different levels of resistances to downy mildew as they were scored through the calculation of their area under diseases progress curve and their two resistance components, the incubation period and the latent period. Significant differences were found between accessions regarding the three criteria (Incubation Period, Latent Period and area under diseases progress curve). Accessions M2a and S938/1 were ranked resistant as they showed the longest Incubation Period (7 days) and Latent Period (12 days) and the lowest area under diseases progress curve (4). Therefore, M24 is the most susceptible accessions as it has presented the highest area under diseases progress curve (34.5) and the shortest Incubation Period (1 day) and Latent Period (3 days). In parallel to this evaluation approach, the accession resistance was confirmed under the field conditions through natural infection by using the tree-leaf method. The high correlation found between detached leaf inoculation method and field screening under natural infection allows us to use this laboratory technique with sureness in further selection works.

— The present work has been carried out to evaluate the diversity of a collection of 78 quinoa accessions developed through recurrent selection from Andean germplasm introduced to Morocco in the winter of 2000. Twenty-three quantitative and qualitative characters were used for the evaluation of genetic diversity and the relationship between the accessions, and also for the establishment of a core collection in Morocco. Important variation was found among the accessions in terms of plant morphology and growth behavior. Data analysis showed positive correlation of the plant height, the plant fresh and the dry weight with the grain yield, while days to flowering was found to be negatively correlated with grain yield. The first four PCs contributed 74.76% of the variability; the first PC showed significant variation with 42.86% of the total variation, PC2 with 15.37%, PC3 with 9.05% and PC4 contributed 7.49% of the total variation. Plant size, days to grain filling and days to maturity are correlated to the PC1; and seed size, inflorescence density and mildew resistance are correlated to the PC2. Hierarchical cluster analysis rearranged the 78 quinoa accessions into four main groups and ten sub-clusters. Clustering was found in associations with days to maturity and also with plant size and seed-size traits.