Cowpea and cassava leaf responses to different levels of water stress using fluorescence and reflectance spectroscopies
- Post by: SOAPHYS-KZ
- 15 juillet 2025
- Comments off
http://dx.doi.org/10.46411/jpsoaphys.2025.C25.01
Section de la parution: Informations de publication
J. P. Soaphys, Vol 4, N°2 (2024) C24A09
Pages : C25A01-1 à C25A01-9
DOI de l’article : http://dx.doi.org/10.46411/jpsoaphys.2025.C25.01
Informations sur les auteurs
Kokou Jean Baptiste Amewotepe1, Kossivi Bienvenu Rodrigue Afoudji1, Essoham Sylvain Adewi1, Milohum Mikesokpo Dzagli1,2 and Messanh Agbeko Mohou1
1Laboratoire de Physique des Matériaux et Composants à Semi-conducteurs, University of Lomé, Lomé, Togo.
2Regional Centre of Excellence on Electricity Management (CERME), University of Lomé, Lomé, Togo.
Corresponding author e-mail : mdzagli@gmail.com
ABSTRACT
Rising temperatures and increasingly recurring droughts are due to global warming. This phenomenon leads to increased water stress in crops and a drop in yield in certain cereal plants and tubers such as cowpea and cassava. Early detection of water stress is essential for the rational use of water resources and to overcome this stress. Several direct measurement techniques are used for this stress detection, such as stomatal conductance, chlorophyll content, soil moisture content, and leaf area, but their implementation is long and tedious. Fluorescence and reflectance spectroscopies could allow early water stress detection in real time. This research aims to evaluate the impact of water status in cowpea and cassava plant leaves using fluorescence and reflectance spectroscopies. Three degrees of stress of 0%, 50%, and 100% of the useful water reserve are induced on the two plants. Reflectance spectra were used to discriminate the different states of the plant. Similarly, a significant difference was observed in the variations of spectral indices such as normalized difference vegetation index, water index, and photochemical reflectance index that are more sensitive to plant water stress, especially in their growth phase. The fluorescence ratios show discrimination between the different plant water treatments. Classification using principal component analysis shows three classes corresponding to the three water treatments induced in the plants. The findings highlight a route of potential leaf-level water stress detection using noninvasive methods such as reflectance for plant health.
Keywords : Water stress, reflectance, fluorescence, cowpea, cassava.
RESUME
L’augmentation des températures et les sécheresses de plus en plus récurrentes sont dues au réchauffement climatique. Ce phénomène entraîne un stress hydrique accru dans les cultures et une baisse de rendement chez certaines céréales et tubercules comme le niébé et le manioc. La détection précoce du stress hydrique est essentielle pour une utilisation rationnelle des ressources en eau et pour surmonter ce stress. Plusieurs techniques de mesures directes sont utilisées pour cette détection de stress telles que la conductance stomatique, la teneur en chlorophylle, la teneur en humidité du sol et la surface foliaire mais leur mise en œuvre est longue et fastidieuse. Les spectroscopies de fluorescence et de réflectance pourraient permettre une détection précoce du stress hydrique en temps réel. Cette recherche vise à évaluer l’impact de l’état hydrique dans les feuilles des plants de niébé et de manioc en utilisant des spectroscopies de fluorescence et de réflectance. Trois degrés de stress de 0%, 50% et 100% de la réserve en eau utile sont induits sur les deux plantes. Des spectres de réflectance ont été utilisés pour discriminer les différents états de la plante. De même, une différence significative a été observée dans les variations des indices spectraux tels que l’indice de végétation par différence normalisée, l’indice hydrique et l’indice de réflectance photochimique qui sont plus sensibles au stress hydrique des plantes, notamment en phase de croissance. Les ratios de fluorescence montrent une discrimination entre les différents traitements hydriques des plantes. La classification par analyse en composantes principales montre trois classes correspondant aux trois traitements hydriques induits dans les plantes. Les résultats mettent en évidence une voie de détection potentielle du stress hydrique au niveau des feuilles à l’aide de méthodes non invasives telles que la réflectance pour la santé des plantes.
Mots-Clés : Radon, SARAD EQF 3200, Concentration d’activité, Niveau de référence
REFERENCES
Achinewhu, S.C., Akah, G.N. (2003) Chemical, functional and sensory properties of processed African yam beans (Sphenostylis stenocarpa) and cowpeas (Vigna unguiculata). Plant Foods Human Nutrition, vol. 58, n°3, pp. 1‑6. doi:10.1023/B:QUAL.0000040364.74521.d8.
Adebayo, W.G. (2023) Cassava production in Africa: A panel analysis of the drivers and trends. Heliyon, vol. 9, pp. e19939. https://doi.org/10.1016/j.heliyon.2023.e19939
Adeniyi, O.I., Adejumo, S.A., Fofana, M. & Adegbehingbe, F.T. (2022). Physiological responses of cowpea simultaneously exposed to water deficit stress and varying light intensities at vegetative and reproductive growth stages. Journal of Plant Stress Physiology, 8:7896, 26-43.
Affrifah, N. S., Phillips, R. D., Saalia, F. K. (2022). Cowpeas: Nutritional profile, processing methods and products—A review. Legume Science, vol. 4, n° 3, pp. 131. https://doi.org/10.1002/leg3.131
Agarwal, V., Vishvendra Raj Singh, B., Marsh, S., Qin, Z., Sen, A., & Kulhari, K. (2025). Integrated remote sensing for enhanced drought assessment: A multi-index approach in Rajasthan, India. Earth and Space Science, 12, e2024EA003639. https://doi.org/10.1029/2024EA003639
Agbodan, K.M.L., Akpavi, S., Agbodan, K.A., Kanda, M., Amegnaglo, K.B., Adrou-Aledji, A., Batawila, K., Akpagana, K. (2020) Description agromorphologique et Détermination du potentiel antioxydant des variétés sous-utilisées et nouvellement introduites de maïs, manioc, niébé et piment dans la région Maritime-Est du Togo. African. Journal, Food Agriculture. Nutrition. Development, vol.20, n°3, pp.15936-15953. doi:10.18697/ajfand.91.18625.O
Ajayi, A.T., Gbadamosi, A.E., Olumekun, V.O. (2018) Screening for drought tolerance in cowpea (Vigna unguiculata L. Walp) at the seedling stage under screen house condition. International Journal of Bioscience. Technology, vol.11, n°1, pp.1-19.
Bakayoko, S., Tschannen, A.C., Nindjin, Dao, D., Girardin, O., Assa, A. (2009) Impact of water stress on fresh tuber yield and dry matter content of cassava (Manihot esculenta Crantz) in Côte d’Ivoire. African Journal of Agricultural Research, vol.4, n°1, pp.021-027.
Bassedik, A. (2021) Ethnobotanical investigation and Morphobiometric Characterization of different Cowpea seeds (Vigna Unguiculata Subsp. Unguiculata (L.) Walp) in the Hoggar region (Algerian Sahara): acquisition And future investment project for food security in Algeria. Algerian Journal of Biosciences, vol.2, n°2, pp.93–110. https://doi.org/10.57056/ajb.v2i2.46
Bedair, H., Alghariani, M.S., Omar, E., Anibaba, Q.A., Remon, M, Bornman, C., Kiboi, S.K., Rady, H.A., Salifu, A.M.A., Ghosh, S., Guuroh, R.T., Sanou, L., Alzain, H.M. (2023) Global Warming Status in the African Continent: Sources, Challenges, Policies, and Future Direction. International Journal of Environmental Research, vol.17, pp.45. https://doi.org/10.1007/s41742-023-00534-w
Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722–738.
Bourgoin, C., Parker L., Martínez-Valle A, Mwongera, C., Läderach P. (2017) Une évaluation spatialement explicite de la vulnérabilité du secteur agricole au changement climatique dans l’Union des Comores. Document de Travail no. 205. Wageningen, Les Pays-Bas: CGIAR Research Program on Climate Change. Agriculture and Food Security (CCAFS).
Brown, D., Poole, L. (2023). Enhanced Plant Species and Early Water Stress Detection Using Visible and Near-Infrared Spectra. In: Smys, S., Tavares, J.M.R.S., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, 1439. Springer, Singapore, vol.765, pp.765–779. https://doi.org/10.1007/978-981-19-9819-5_55
Burns, A., Gleadow, R., Cliff, J., Zacarias, A., Cavagnaro, T. (2010) Cassava: The Drought, War and Famine Crop in a Changing World. Sustainability, vol.2, pp.3572-3607. https://doi.org/10.3390/su2113572
Cerovic, Z.G., Samson, G., Morales, F., Tremblay, N., Moya, I. (1999) Ultraviolet-induced fluorescence for plant monitoring: present state and prospects. Agronomie, vol.19, n°7, pp.543-578. https://doi.org/10.1051/agro:19990701
Chafika, H., Nsarellah, N., Keltoum, E., Said, M., Udupa, S. (2014) Effet du stress hydrique sur les critères physiologiques et biochimiques chez neuf génotypes de pois chiche (Cicer arietinum L). National. Technology, n°11, pp.8-16.
Chappelle, E.W., Wood, F.M., McMurtrey, J.E., Newcomb, W.W. (1984) Laser-induced fluorescence of green plants.1: A technique for the remote detection of plant stress and species differentiation. Applied Optics, vol. 23, n°1, pp.134‑138. doi:10.1364/AO.23.000134.
Chevallier-Le Guyader, M.F., Bock, F. (2020) Présentation nourrir 10 milliards d’êtres humains. Raison Présente, vol.213, n°1, pp.3‑9. doi: 10.3917/rpre.213.0003.
Coelho, E., Ballesteros, L.F., Domingues, L., Vilanova, M., Teixeira, J.A. (2020) Production of a Distilled Spirit Using Cassava Flour as Raw Material: Chemical Characterization and Sensory Profile. Molecules, vol.25, n°14, pp.3228. https://doi.org/10.3390/molecules25143228
Combres, J.C., Le Mezo, L., Mete, M., Bourjon, B. (1999) Réserve utile et mesures d’humidité. Difficulté de calage des modèles de bilan hydrique. Agriculture et Développement, vol.24, pp.39-47.
Cui, M., Xiong, S., Yang, N., Wang, Y., Wang, Z., Luo, M., Yao, C., Deguchi, Y. (2024) Applications of laser-induced breakdown spectroscopy in industrial measurement and monitoring: Multi-technology combination. Applied Spectroscopy Review, vol.0, n°0, pp.1‑49. doi:10.1080/05704928.2024.2398196.
Dai, R. , Chen, S., Xu, X., Wang Z. (2023)Comparison and analysis of hyperspectral vegetation indices for recognizing water stress of soybean. Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125512K. https://doi.org/10.1117/12.2668138
Das Choudhury S, Saha S, Samal A, Mazis A and Awada T (2023) Drought stress prediction and propagation using time series modeling on multimodal plant image sequences. Front. Plant Sci. 14:1003150. doi: 10.3389/fpls.2023.1003150
Djohy, G.L., Bouko, B.S., Djohy, G.P.J., Dossou, J., Yabi,A. (2023) Contribution des résidus de culture à la réduction du déficit alimentaire des troupeaux de ruminants dans l’Ouémé Supérieur au Bénin. Cahiers d’Agriculture, vol.32, pp.13. doi:10.1051/cagri/2023007.
Doussan, C., Ruedessols, C., Cousin, I. (2017) La Réserve Utile : concepts, outils, controverses. Atelier du RMT Sols et Territoires, Orléans, France.
El Fakir, C. (2020) Imagerie hyperspectrale active basée sur un laser QCL largement accordable dans l’infrarouge moyen. Optique PhD Thesis, Université de Rennes.
Fu, Y. X., Liu, S. Y., Guo, W. Y., Dong, J., Nan, J. X., Lin, H. Y., Mei, L. C., Yang, W. C., & Yang, G. F. (2022). In vivo diagnostics of abiotic plant stress responses via in situ real-time fluorescence imaging. Plant physiology, 190(1), 196–201. https://doi.org/10.1093/plphys/kiac273
Gamon, J. A., Peñuelas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35–44.
Gasparini, K., Rafael, D.D., Peres, L.E.P., Ribeiro, D.M., Zsögön, A. (2024). Agriculture and Food Security in the Era of Climate Change. In: Priyadarshan, P.M., Jain, S.M., Penna, S., Al-Khayri, J.M. (eds) Digital Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-031-43548-52.
Ghobadi, M., Badehian, Z. Assessment of agricultural drought severity using multi-temporal remote sensing data in Lorestan region. Sci Rep 15, 18528 (2025). https://doi.org/10.1038/s41598-025-03087-4
Godfray, H.C.J.; Beddington, J.R.; Crute, I.R., Haddad, L.; Lawrence, D.; Muir, J.F., Pretty, J., Robinson, S.; Thomas, S.M., Toulmin, C. (2010) Food security: The challenge of feeding 9 billion people. Science, vol.327, n°812. doi:10.1126/science.1185383.
Gupta, S., Kumar, S. (2023) A state-of-the-art review of the deep soil mixing technique for ground improvement. Innovative Infrastructure Solutions, 8:129. https://doi.org/10.1007/s41062-023-01098-6
Horn, L.N., Shimelis, H. (2020) Production constraints and breeding approaches for cowpea improvement for drought-prone agro-ecologies in Sub-Saharan Africa. Annals of Agriculture. Sciences, vol.65, n°1, pp.83‑91. https://doi.org/10.1016/j.aoas.2020.03.002.
Ihuoma, S.O., Madramootoo, C.A. (2019) Sensitivity of spectral vegetation indices for monitoring water stress in tomato plants. Computers Electronics in Agriculture 163:104860.https://doi.org/10.1016/j.compag.2019.104860.
Ihuoma, S.O.C., Madramootoo, A. (2017) Recent advances in crop water stress detection. Computers and Electronics in Agriculture, vol.141, pp.267‑275.doi: 10.1016/j.compag.2017.07.026.
Immanuel, S., Jaganathan, D., Prakash, P., Sivakumar, S. (2024). Cassava for Food Security, Poverty Reduction and Climate Resilience: A Review. Indian Journal of Ecology vol.51, n°1, pp.21-31. https://doi.org/10.55362/IJE/2024/4191
Jones, E.R., Bierkens, M.F.P., van Vliet, M.T.H. (2024) Current and future global water scarcity intensifies when accounting for surface water quality. Nature Climate Change, vol.14, pp.629–635. https://doi.org/10.1038/s41558-024-02007-0
Jones, H.G. (2007) Monitoring plant and soil water status: established and novel methods revisited and their relevance to studies of drought tolerance. Journal of Experimental Botany, vol.58, n°2, pp.119‑130.doi: 10.1093/jxb/erl118.
Katsoulas, N., Elvanidi, A., Ferentinos, K.P., Kacira, M., Bartzanas,T., Kittas, C. (2016) Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review. Biosystems engineering, vol.151, pp. 374‑398. doi:10.1016/j.biosystemseng.2016.10.003.
Konapala, G., Mishra, A.K., Wada, Y., Mann, M.E. (2020) Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nature Communications, vol.11, n°1, pp.3044. https://doi.org/10.1038/s41467-020-16757-w
Kothari, S., Beauchamp-Rioux, R., Blanchard, F., Crofts, A.L., Girard, A., Guilbeault-Mayers, X., Hacker, P.W., Pardo, J., Schweiger, A.K., Demers-Thibeault, S., Bruneau, A., Coops, N.C., Kalacska, M., Vellend, M. and Laliberté, E. (2023), Predicting leaf traits across functional groups using reflectance spectroscopy. New Phytologist, vol.238, pp.549-566. https://doi.org/10.1111/nph.18713.
Kouassi, A.M., Kouao, J.M., Kouakou, K.E. (2022) Caracterisation intra-annuelle de la variabilité climatique en Côte d’Ivoire. Bulletin de l’association des Géographes Francais, vol.99, n°2, pp.289-306. doi: 10.4000/bagf.9534.
Legendre, R., Basinger, N. T., & van Iersel, M. W. (2021). Low-Cost Chlorophyll Fluorescence Imaging for Stress Detection. Sensors (Basel, Switzerland), 21(6), 2055. https://doi.org/10.3390/s21062055
Li, C., Czyż, E.A., Halitschke, R., Baldwin, I.T., Schaepman, M.E., Schuman, M.C. (2023) Evaluating the potential of leaf reflectance spectra to monitor plant genetic variation. Plant Methods, vol.19, pp.108. https://doi.org/10.1186/s13007-023-01089-9
Li, Z.K., Li, H.L., Gong, X.W., Wang H.-F., Hao G.-Y. (2024b). Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery. Plant Methods 20, 184. https://doi.org/10.1186/s13007-024-01312-1
Lichtenthaler, H.K. (1988). In Vivo Chlorophyll Fluorescence as a Tool for Stress Detection in Plants. In: Lichtenthaler, H.K. (eds) Applications of Chlorophyll Fluorescence in Photosynthesis Research, Stress Physiology, Hydrobiology and Remote Sensing. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-2823-7_16
Lichtenthaler,H. K., Buschmann, C. (1987).Chlorophyll Fluorescence Spectra of Green Bean Leaves. Journal Plant Physiological, vol.129, n°1, pp.137‑147.doi: 10.1016/S0176-1617(87)80110-4.
Lins, E.C., Belasque, J., Marcassa, L.G. (2009) Detection of citrus canker in citrus plants using laser induced fluorescence spectroscopy, Precision Agriculture. vol.10, n°4, pp.319‑330.doi: 10.1007/s11119-009-9124-2.
Mallick, J., Alqadhi, S., Alsubih, M., Othman, A. Multi-scale analysis of vegetation response to drought dynamics in Saudi Arabia. Theor Appl Climatol 156, 350 (2025). https://doi.org/10.1007/s00704-025-05580-2
Manghwar, H., Hussain, A., Alam, I., Khoso, M.A., Ali, O., Liu, F. (2024) Waterlogging stress in plants: Unraveling the mechanisms and impacts on growth, development, and productivity. Journal Environmental and Experimental Botany, vol.224, pp.105824. https://doi.org/10.1016/j.envexpbot.2024.105824
Marcassa, L.G., Gasparoto, M.C.G., Belasque, J., Lins, E. C., Dias Nunes, F., Bagnato, V.S. (2006) Fluorescence spectroscopy applied to orange trees. Laser Physics, 16(5):884‑888. doi: 10.1134/S1054660X06050215.
McKenzie, F.C., Williams, J. (2015) Sustainable food production: constraints, challenges and choices by 2050. Food security, vol.7, pp.221–233. https://doi.org/10.1007/s12571-015-0441-1.
Mohidin, S. R. N. S. P., Moshawih, S., Hermansyah, A., Asmuni, M. I., Shafqat, N., Ming, L.C. (2023). Cassava (Manihot esculenta Crantz): A Systematic Review for the Pharmacological Activities, Traditional Uses, Nutritional Values, and Phytochemistry. Journal of evidence-based integrative medicine, vol.28, pp.1-26. https://doi.org/10.1177/2515690X231206227
Moustaka, J., & Moustakas, M. (2023). Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis. Biosensors, 13(8), 796. https://doi.org/10.3390/bios13080796
Munaweera, T.I.K., Jayawardana, N.U., Rajaratnam, R., Dissanayake, N. (2022) Modern plant biotechnology as a strategy in addressing climate change and attaining food security. Agric & Food Secur, vol.11, pp.26 . https://doi.org/10.1186/s40066-022-00369-2
Mussard, H., Brialix, L., Barraud-Roussel, Y., Cannavo, P. (2024) Caractérisation par capteurs du stress hydrique des cultures selon une approche intégrée sol-plante-microclimat. Conséquences sur la stratégie d’irrigation. Étude et Gestion des Sols, vol.31, pp.161-177.
Nguinambaye, M.M., Nana, R., Djinet, I.A., Tamini, Z. (2020) Quelques paramètres physiologiques et constituants biochimiques des organes de la lentille de terre (Macrotyloma geocarpum) en conditions de stress hydrique. International Journal of Biology. Chemestry. Science, vol.14, n°4, pp.1228-1240. doi: 10.4314/ijbcs.v14i4.6.
Niyonsenga, S., Eziz, A., Kurban, A., Yuan, X., Umwali, E. D., Azadi, H., Hakorimana, E., Umugwaneza, A., Fidelis, G. D., Nsanzabaganwa, J., & Nzabarinda, V. (2024). Spatiotemporal Analysis of Drought Characteristics and Their Impact on Vegetation and Crop Production in Rwanda. Remote Sensing, 16(8), 1455. https://doi.org/10.3390/rs16081455
Ogawa, T.; Tamaki, M.; Usui, T.; Hikosaka, K. Hyperspectral Image Extraction to Evaluate the Photosynthetic and Stress Status of Plants, Using a Photochemical Reflectance Index (pri). Sci. Hortic. 2024, 336, 113349.
Omotoso, A.B., Letsoalo, S., Olagunju, K.O., Tshwene, S.C., Omotayo, A.O. (2023) Climate change and variability in sub-Saharan Africa: A systematic review of trends and impacts on agriculture. Journal of Cleaner Production, vol.414, pp.137487. https://doi.org/10.1016/j.jclepro.2023.137487.
Park, B., Wi, S., Chung, H., & Lee, H. (2024). Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops. Sensors, 24(5), 1442. https://doi.org/10.3390/s24051442
Patiluna, V., Owen, J., Jr., Maja, J. M., Neupane, J., Behmann, J., Bohnenkamp, D., Borra-Serrano, I., Peña, J. M., Robbins, J., & de Castro, A. (2025). Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants. Remote Sensing, 17(2), 285. https://doi.org/10.3390/rs17020285
Peñuelas, J., Filella, I. (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Science, vol.3, n°4, pp. 151‑156. doi: 10.1016/S1360-1385(98)01213-8.
Peñuelas, J., Inoue, Y. (1999) Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves. Photosynthetica, vol.36, n°3, pp.355‑360. doi: 10.1023/A:1007033503276.
Priya, M., Siddique, K.H.M., Dhankhar, O.P., Prasad, P.V.V., Hanumantha Rao, B., Nair, R.M., Nayyar, H. (2018) Molecular breeding approaches involving physiological and reproductive traits for heat tolerance in food crops. Indian Journal of Plant Physiology, vol.23, n°4, pp.697‑720. doi: 10.1007/s40502-018-0427-z.
Poudel, S., Valsala Sankarapillai, L., Sivarathri, B. S., Hosahalli, V., Harkess, R. L., & Bheemanahalli, R. (2025). Characterization of Cowpea Genotypes for Traits Related to Early-Season Drought Tolerance. Agriculture, 15(10), 1075. https://doi.org/10.3390/agriculture15101075
Ranulfi, A.C., Cardinali, M.C.B., Kubota, T.M.K., Freitas-Astua, J., Ferreira, E.J., Bellete, B.S., da Silva, M.F.G.F., Boas, P.R.V., Magalhaes, A.B., Debora, M.B.P., Milori. (2016) Laser-induced fluorescence spectroscopy applied to early diagnosis of citrus Huanglongbing. Biosystems Engineering,144:133‑144.doi:10.1016/j.biosystemseng.2016.02.010.
Ravelombola, W., Shi, A., Chen, S., Xiong, H., Yang, Y. Cui, Q., Olaoye, D., Mou, B. (2020) Evaluation of cowpea for drought tolerance at seedling stage. Euphytica, vol.216, pp.123. https://doi.org/10.1007/s10681-020-02660-4
Robinson, A., Lehmann, J., Barriopedro, D., Coumou, D. (2021) Increasing heat and rainfall extremes now far outside the historical climate. Climate and Atmospheric Science, vol.4, pp.45 https://doi.org/10.1038/s41612-021-00202-w.
Safdar, M., Shahid, M.A., Sarwar, A., Rasul, F., Majeed, M.D., Sabir, R.M. (2023) Crop Water Stress Detection Using Remote Sensing Techniques. Environmental Sciences Proceedings, vol.25, pp.20. https://doi.org/10.3390/ECWS-7-14198
Sebetha, E.T., Modi, A.T., Owoeye, L.G. (2015) Cowpea crude protein as affected by cropping system, site, and nitrogen fertilization. Journal Agriculture Science, vol.7, n°1, pp.224. doi:10.5539/jas.v7n1p224.
Seleiman, M.F., Al-Suhaibani, N., Ali, N., Akmal, M., Alotaibi, M., Refay, Y., Dindaroglu, T., Abdul-Wajid, H.H., Battaglia, M.L. (2021) Drought Stress Impacts on Plants and Different Approaches to Alleviate Its Adverse Effects. Plants, vol.10, pp.259. https://doi.org/10.3390/plants10020259.
Silva-Perez, V., Molero, H.G., Serbin, S.P., Condon, A.G., Reynolds, M.P., Furbank, R.T., John, R. (2018). Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. Journal of Experimental. Botany, vol.69, n°3, pp.483‑496. doi:10.1093/jxb/erx421.
Suárez, L., Zarco-Tejada, P. J., Berni, J. A. J., González-Dugo, V., & Fereres, E. (2008). Modelling PRI for water stress detection using radiative transfer models. Remote Sensing of Environment, 112(3), 560–575.
Sun, J.Q., Zhao, X.Z., Liang, C.Y., Yang, Z.X., Liu, Y., Qi, D.-P. (2023) The monitoring of plant physiology and ecology: From materials to flexible devices. Chinese Journal of Analytical Chemistry, vol.51, n°2, pp.100211. https://doi.org/10.1016/j.cjac.2022.100211
Torres, J., Peters, M., Montoya, C.A., (2019) Boiling influences the nutritional value of three seed cowpea (Vigna unguiculata) varieties using in vivo and in vitro methods. Food Chemestry, vol.297, pp.124940. https://doi.org/10.1016/j.foodchemistry .2019.06.007.
United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024: Ten Key Messages.
Van Dijk, M., Morley, T., Rau, M.L., Saghai Y. (2021) A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nature Food, vol.2, pp494–501. https://doi.org/10.1038/s43016-021-00322-9
Vernier, P., N’zué, B., Zakhia-Rozis, N. (2018) Le manioc, entre culture alimentaire et filière agro-industrielle. Versailles: Ed. Quae; CTA, pp.208 Agricultures tropicales en poche. doi :10.35690/978-2-7592-2708-2.
Weng, S., Ma, J., Tao, W., Tan, Y., Pan, M., Zhang, Z., Huang, L., Zheng, L., & Zhao, J. (2023). Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion. Frontiers in plant science, 14, 1073530. https://doi.org/10.3389/fpls.2023.1073530
Wu, G., Chen, J., Shi, X., Kim, J.-S., Xia, J., & Zhang, L. (2022). Impacts of global climate warming on meteorological and hydrological droughts and their propagations. Earth’s Future, vol.10, pp.e2021EF002542. https://doi.org/10.1029/2021EF002542
Zait, Y., Shemer, O.E. & Cochavi, A. (2024). Dynamic responses of chlorophyll fluorescence parameters to drought across diverse plant families. Physiologia Plantarum, 176(5), e14527. Available from: https://doi.org/10.1111/ppl.14527
Zhuang J, Wang Y, Chi Y, Zhou L, Chen J, Zhou W, Song J, Zhao N, Ding J. 2020. Drought stress strengthens the link between chlorophyll fluorescence parameters and photosynthetic traits. PeerJ 8:e10046 https://doi.org/10.7717/peerj.10046