Adama Ndour

Centre d'Etude Regional pour l'Amelioration de l'Adaptation a la Secheresse

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Mikaël Lucas

Institut de Recherche pour le Développement

Christophe Pradal

Genetic Improvement and Adaptation of Mediterranean and Tropical Plants

Josiane Seghieri

Institut de Recherche pour le Développement

Jamie R. Flynn

Hunter Water

Cathy Clermont‐Dauphin

Ecologie fonctionnelle & biogéochimie des sols & des agro-systèmes

Krithika Anbazhagan

International Crops Research Institute for the Semi-Arid Tropics

Srikanth Mallayee

International Crops Research Institute for the Semi-Arid Tropics

Jana Kholová

International Crops Research Institute for the Semi-Arid Tropics

Olivier Roupsard

Centre de Coopération Internationale en Recherche Agronomique pour le Développement

Alain Audebert

Centre de Coopération Internationale en Recherche Agronomique pour le Développement

Cooperative Institutions

Centre de Coopération Internationale en Recherche Agronomique pour le Développement

Université de Montpellier

Agropolis International

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Institut Agro Montpellier

Institut de Recherche pour le Développement

Institut Sénégalais de Recherches Agricoles

L'Institut Agro

International Crops Research Institute for the Semi-Arid Tropics

LMI Adaptation des Plantes et Microorganismes Associés aux Stress Environnementaux

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Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals

Keerthi Chadalavada Krithika Anbazhagan Adama Ndour Sunita Choudhary William M. Palmer

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.

Grain Quality

10.20944/preprints202202.0335.v1

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Citations (1)

How far does the tree affect the crop in agroforestry? New spatial analysis methods in a Faidherbia parkland

Agriculture Ecosystems & Environment (2020)

Olivier Roupsard Alain Audebert Adama Ndour Cathy Clermont‐Dauphin Yélognissè Agbohessou

Affect

Tree (set theory)

10.1016/j.agee.2020.106928

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Citations (62)

Using UAV and geostatistics to upscale crop yield in heterogeneous agro-silvo-pastoral system

Yélognissè Agbohessou Alain Audebert Adama Ndour Mame Sokhna Sarr Christophe Jourdan

The beneficial effect of Faidherbia albida on the yield of certain associated crops has been demonstrated for long and is often characterized by a distance-decay pattern. While several approaches have tested the spatial extent assessment of tree influence, none of them has been designed either to capture the directional variations or to address the park effect at the landscape scale. Recently, Roupsard et al. (2020) proposed an approach based on multispectral (MS) UAV (Unmanned Aerial Vehicle) imagery and geostatistics to bridge this gap. In the present study, we extended their study by proposing a new application of their approach to groundnut crop and validation for millet crop. In addition, we tested improvements of the method, using several MS images along the crop cycle.In a typical F. albida parkland (Niakhar, Senegal), groundnut and millet under-crops of agroforestry plots of approximately 1-ha and 2-ha respectively, have been harvested. On each plot, groundnut and millet traits were measured at three different positions from six F. albida trees (under crown "S"; crown edge "B" and far from the crown "H"). We found that F. albida improves the haulm yield of the groundnut crops under its crown by about 50%. However, unlike its strong effect on millet, it does not significantly affect the groundnut pod yield. Through geostatistical analysis of multi-spectral, centimetric-resolution images obtained from the UAV flights carried out during the wet season, we observed that F. albida affects the groundnut NDVI signal up to 9.8-m and the NDVI of millet up to 18-m. We found statistically significant, positive correlations between groundnut pod yield and MSAVI2, NDVI (r2 = 0.73; RMSE = 9.8) first, and between groundnut haulm yield and MSAVI2 (r2 = 0.85; RMSE = 5.81). For millet, the multiple linear regression model is able to explain 74% of the millet yield variability (RMSE=20.48) using millet+weed MSAVI2 and NDVI. We used the regression model to upscale groundnut pod and haulm yield maps at the whole-plot scale. Compared with groundtruth, the error was only by 8% and 13% for groundnut pod and haulm yield, respectively. Using a geostatistical proxy for the sole crop, the crop-partial Land Equivalent Ratio (LERcp) was estimated at 1.02 for pod yield and 1.05 for haulm yield.

Geostatistics

Agroecosystem

10.5194/egusphere-egu22-11773

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Role of auxin during intercellular infection of Discaria trinervis by Frankia

Frontiers in Plant Science (2014)

Leandro Imanishi Francine Perrine‐Walker Adama Ndour Alice VayssiÃ ̈res Geneviève Conéjéro

Nitrogen-fixing nodules induced by Frankia in the actinorhizal plant Discaria trinervis result from a primitive intercellular root invasion pathway that does not involve root hair deformation and infection threads. Here, we analyzed the role of auxin in this intercellular infection pathway at the molecular level and compared it with our previous work in the intracellular infected actinorhizal plant Casuarina glauca. Immunolocalisation experiments showed that auxin accumulated in Frankia-infected cells in both systems. We then characterized the expression of auxin transporters in D. trinervis nodules. No activation of the heterologous CgAUX1 promoter was detected in infected cells in D. trinervis. These results were confirmed with the endogenous D. trinervis gene, DtAUX1. However, DtAUX1 was expressed in the nodule meristem. Consistently, transgenic D. trinervis plants containing the auxin response marker DR5:VENUS showed expression of the reporter gene in the meristem. Immunolocalisation experiments using an antibody against the auxin efflux carrier PIN1, revealed the presence of this transporter in the plasma membrane of infected cells. Finally, we used in silico cellular models to analyse auxin fluxes in D. trinervis nodules. Our results point to the existence of divergent roles of auxin in intercellularly- and intracellularly-infected actinorhizal plants, an ancestral infection pathways leading to root nodule symbioses.

Frankia

Actinorhizal plant

10.3389/fpls.2014.00399

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Citations (24)

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

Sensors (2022)

Keerthi Chadalavada Krithika Anbazhagan Adama Ndour Sunita Choudhary William E. Palmer

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Grain Quality

10.3390/s22103710

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Citations (33)

Virtual Plants Need Water Too: Functional-Structural Root System Models in the Context of Drought Tolerance Breeding

Frontiers in Plant Science (2017)

Adama Ndour Vincent Vadez Christophe Pradal Mikaël Lucas

Developing a sustainable agricultural model is one of the great challenges of the coming years. The agricultural practices inherited from the Green Revolution of the 1960s show their limits today, and new paradigms need to be explored to counter rising issues such as the multiplication of climate-change related drought episodes. Two such new paradigms are the use of functional-structural plant models to complement and rationalize breeding approaches and a renewed focus on root systems as untapped sources of plant amelioration. Since the late 1980s, numerous functional and structural models of root systems were developed and used to investigate the properties of root systems in soil or lab-conditions. In this review, we focus on the conception and use of such root models in the broader context of research on root-driven drought tolerance, on the basis of root system architecture phenotyping. Such models result from the integration of architectural, physiological and environmental data. Here, we consider the different phenotyping techniques allowing for root architectural and physiological study and their limits. We discuss how QTL and breeding studies support the manipulation of root system architecture as a way to improve drought resistance. We then go over the integration of the generated data within architectural models, how those architectural models can be coupled with functional hydraulic models, and how functional parameters can be measured to feed those models. We then consider the assessment and validation of those hydraulic models through confrontation of simulations to experimentations. Finally, we discuss the up and coming challenges facing root systems functional-structural modeling approaches in the context of breeding.

Root (linguistics)

Root system

Drought Tolerance

10.3389/fpls.2017.01577

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Citations (30)

Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions

Agronomy (2021)

Boubacar Gano Joseph Sékou B. Dembele Adama Ndour Delphine Luquet Grégory Beurier

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.

10.3390/agronomy11050850

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Citations (49)

The role of hydraulics FSPMs in the context of root breeding : a case study on Pearl Millet

HAL (Le Centre pour la Communication Scientifique Directe) (2018)

Adama Ndour Christophe Pradal Vincent Vadez Sixtine Passot Yann Guédon

Developing a sustainable agricultural model is one of the great challenges of the coming years. The agricultural practices inherited from the Green Revolution of the 1960s show their limits today, and new paradigms need to be explored in the context of counter rising issues such as the multiplication of climate-change related drought episodes. Two such new paradigms are the use of functional-structural plant models to complement and rationalize breeding approaches and a renewed focus on root systems as untapped sources of plant improvement. Since the late 1980s, numerous functional and structural models of root systems were developed and used to investigate the properties of root systems in soil or lab-conditions. In this talk, we present a review on the use of multiscale functional-structural hydraulic root models in the context of drought tolerance breeding. We discuss how root models predictions can be linked to breeding studies to improve plant resistance to drought and how they can be validated to demonstrate models reliability and use. To illustrate this topic, we present a new structural model of pearl millet root system growth dynamics, combining stochastic and data-driven modules. The model is capable of simulating the development of observed root phenotypic variability of two millet genotypes chosen for their contrasted root traits. Model description, principle, assumptions, formalism and simulations will be presented during the talk.

Root (linguistics)

Root system

Drought Tolerance

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