Drought Stress Responses of Four Contrasting Provenances of Argania spinosa

Mouafik, Mohamed; Chakhchar, Abdelghani; Ouajdi, Mohamed; Antry, Salwa El; Ettaleb, Ismail; Aoujdad, Jalila; Aboudi, Ahmed El

doi:10.3390/environsciproc2022016025

The 2nd International Laayoune Forum on Biosaline Agriculture 2022

DOI: 10.3390/environsciproc2022016025

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Drought Stress Responses of Four Contrasting Provenances of Argania spinosa

Mohamed Mouafik

Abdelghani Chakhchar

Mohamed Ouajdi

et al.

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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Cited by 3 publications

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Ecophysiology of Four Mediterranean Forest Species

Mohamed,

Oumaima,

Mohamed

et al. 2023

Ann Biomed Sci Eng

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Forеst dеgradation has bеcomе incrеasingly pronouncеd in rеcеnt timеs due to shifts in climatе pattеrns and prolongеd drought pеriods. This invеstigation aims to cultivatе high-quality vеgеtation and gain insights into thеir еcophysiological rеsponsеs undеr conditions of watеr strеss. Spеcifically, wе conductеd еxpеrimеnts on 6-month-old individuals from two dеciduous spеciеs (Quеrcus subеr and Cеratonia siliqua) and two conifеrs (Tеtraclinis articulata and Cеdrus at-lantica), subjеcting thеm to watеr strеss conditions. Wе mеasurеd and analyzеd both thе basic (Ψb) and minimum (Ψm) lеaf watеr potеntials, factoring in climatic variablеs for all four forеst spеciеs. Our findings rеvеal that Quеrcus subеr еxhibits morе nеgativе valuеs, with a basic lеaf watеr potеntial of -0. 42 MPa and a minimum lеaf watеr potеntial of -1. 43 MPa, comparеd to thе othеr studiеd forеst spеciеs. On thе contrary, Cеdrus atlantica displays lеss nеgativе valuеs for thе minimum lеaf watеr potеntial, rеcording -0. 89 MPa. Thеsе outcomеs еnablе us to idеntify thе spеciеs displaying grеatеr rеsiliеncе against watеr strеss and climatе fluctuations. Nеvеrthеlеss, they also prompt broadеr inquiriеs into thе undеrlying mеchanisms govеrning watеr utilization in forеst flora.

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Ecophysiology of Four Mediterranean Forest Species

Mohamed,

Oumaima,

Mohamed

et al. 2023

Ann Biomed Sci Eng

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Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data

Mouafik,

Fouad,

El Aboudi

2024

AgriEngineering

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In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original CHIRPS data with environmental variables, demonstrating enhanced accuracy aligned with ground rain gauge observations after residual correction. Furthermore, we explored the performance of range machine learning algorithms, encompassing XGBoost, GBDT, RF, DT, SVR, LR and ANN, in predicting the Leaf Area Index (LAI) and crop yield of Argane trees using condition index-based drought indices such as PCI, VCI, TCI and ETCI derived from multi-sensor satellites. The results demonstrated the superiority of XGBoost in estimating these parameters, with drought indices used as input. XGBoost-based crop yield achieved a higher R2 value of 0.94 and a lower RMSE of 6.25 kg/ha. Similarly, the XGBoost-based LAI model showed the highest level of accuracy, with an R2 of 0.62 and an RMSE of 0.67. The XGBoost model demonstrated superior performance in predicting the crop yield and LAI estimation of Argania sinosa, followed by GBDT, RF and ANN. Additionally, the study employed the Combined Drought Index (CDI) to monitor agricultural and meteorological drought over two decades, by combining four key parameters, PCI, VCI, TCI and ETCI, validating its accuracy through comparison with other drought indices. CDI exhibited positive correlations with VHI, SPI and crop yield, with a particularly strong and statistically significant correlation observed with VHI (r = 0.83). Therefore, CDI was recommended as an effective method and index for assessing and monitoring drought across Argane forest stands area. The findings demonstrated the potential of advanced machine learning models for improving precipitation data resolution and enhancing agricultural drought monitoring, contributing to better land and hydrological management.

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Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review

Mouafik,

Chakhchar,

Fouad

et al. 2024

Geographies

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This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts.

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Drought Stress Responses of Four Contrasting Provenances of Argania spinosa

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 3 publications

References 6 publications

Ecophysiology of Four Mediterranean Forest Species

Ecophysiology of Four Mediterranean Forest Species

Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data

Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review

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