Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal

Fall, Cheikh Modou Noreyni; Lavaysse, Christophe; Herve, Kerdiles; Drame, Mamadou Simina; Roudier, Philippe; Gaye, Amadou Thierno

doi:10.1016/j.crm.2021.100331

Cited by 20 publications

(23 citation statements)

References 92 publications

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“…These models mainly depend on the data collected in the field, as well as observed weather data. The accuracy of such models also depends on accurate descriptions of crop management practices (e.g., crop variety, sowing date, fertilization, and irrigation), although collecting such data in a sufficiently accurate manner is difficult at the regional scale [23]. Furthermore, several years of experimental data are necessary to train and calibrate models to the local environmental conditions for these crop models, and when they are applied in other regions, they have to be recalibrated [23].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the limitations of these models, statistical models, such as multiple linear regression, have been widely utilized to link crop yields to climate variables [24,25] or even intermediate output variables from process-based crop models [26]. Despite not being directly based on the mechanisms of plant growth, statistical models can effectively predict crop production [23]. The main benefits of statistical models are their limited dependence on field calibration data and their clear assessment of model uncertainties [27].…”

Section: Introductionmentioning

confidence: 99%

“…The main benefits of statistical models are their limited dependence on field calibration data and their clear assessment of model uncertainties [27]. Statistical models typically perform better as the availability and quality of observable data improve [23].…”

Section: Introductionmentioning

confidence: 99%

“…The CST has been effectively applied to growth monitoring and yield forecasting of some main crops in northeast China [30]. Fall et al (2021) also used the CST to predict millet yield at a regional scale in Senegal with input data containing weather data combined with variables derived from remote sensing indicators (NDVI) [23]. The CST enables simple examination of data quality, analysis of crop yield time trend, and construction of crop yield forecasting models through three methods: (1) multivariate regression analysis; (2) scenario analysis, which is a method of forecasting that looks for the previous years that are most similar to the current year based on a set of indicators and combines their yields [23]; and (3) the moving average analysis model, which is based on the average yields of the most recent years preceding the target year.…”

Section: Introductionmentioning

confidence: 99%

“…The CST calculates a number of statistics that facilitate the choice of the best crop yield forecast model for a given region and time of prediction. Another advantage of the CST is its ability to rapidly test multiple models [23,28].…”

Section: Introductionmentioning

confidence: 99%

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Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

et al. 2022

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Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

et al. 2022

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Assessment of Basin‐Scale Concurrent Dry and Wet Extreme Dynamics Under Multimodel CORDEX Climate Scenarios

Swain,

Mishra,

Chatterjee

2024

Intl Journal of Climatology

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The impact of extreme events in risk analysis depends on factors such as magnitude, duration, timing and whether the system recovers fully before the next event occurs. While previous studies have primarily examined the drivers and characteristics of individual extremes, less focus has been given to the concurrent or compounding nature of extremes across adjacent seasons. Thus, understanding the dynamics of such compound extremes, particularly dryness and wetness, is crucial. To address these concerns, a Multi Scalar Drought Index (MSDI) is formulated using precipitation and temperature data from three river basins (Brahmani, Baitarani and Cauvery) of eastern and southern India. The combinations of dryness and wetness, such as Dry‐Dry, Dry‐Wet, Wet‐Dry and Wet‐Wet, between consecutive seasons are analysed across four seasons (summer, rainy, autumn and winter). The prolonged dryness/wetness along with dry/wet year are evaluated from baseline (1979–2018) to projected COrdinated Regional Climate Downscaling Experiment (CORDEX) future period (2020–2099). The spatio‐temporal variations in intra‐annual dry‐wet extremes are identified using the Mann–Kendall test. The results suggest that the eastern Indian river basins, particularly the Brahmani and Baitarani basins, experience more frequent occurrences of compounding dryness‐wetness compared to Cauvery river which is a southern Indian basin. Future scenarios indicate a trend towards dryness during the monsoon season in Brahmani and Baitarani basins, with frequent wet extremes in late autumn and winter. Abrupt transitions between dryness and wetness are prevalent during the Rainy‐Autumn and Autumn‐Winter seasons in Brahmani and Baitarani basins. The increased frequency of compound dry‐wet extremes poses significant socio‐economic risks, including reduced agricultural productivity, water management challenges and heightened vulnerability of local livelihoods dependent on consistent water availability. The results of this study provide a scientific reference for sustainable agriculture and water resource management to predict future seasonal dry and wet alternations and develop effective mitigation strategies.

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Climate Change, Gendered Vulnerabilities and Adaptation Strategies: A Participatory Research in West Africa

Osman,

Ayanlade

2024

Climate Change and Regional Socio-Economic Systems in the Global South

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Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal

Cited by 20 publications

References 92 publications

Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

Assessment of Basin‐Scale Concurrent Dry and Wet Extreme Dynamics Under Multimodel CORDEX Climate Scenarios

Climate Change, Gendered Vulnerabilities and Adaptation Strategies: A Participatory Research in West Africa

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