Climate change and variability in sub-Saharan Africa: A systematic review of trends and impacts on agriculture

Omotoso, Abeeb Babatunde; Letsoalo, Simon; Olagunju, Kehinde Oluseyi; Tshwene, Christopher; Omotayo, Abiodun Olusola

doi:10.1016/j.jclepro.2023.137487

Cited by 52 publications

(9 citation statements)

References 51 publications

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“…The monitoring of different types of agricultural areas might become increasingly important for Sub-Saharan Africa due to climate change affecting agricultural productivity, especially for smallholder farms [114,115]. Due to the relatively high spatial resolution (10 m) of the classification results of this study, the classification approach used can also be applied for monitoring smallholder farms in Ethiopia and comparable regions and, thus, contribute to developing sustainable agricultural management in these regions.…”

Section: Discussionmentioning

confidence: 99%

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Eisfelder,

Boemke,

Gessner

et al. 2024

Remote Sensing

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Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management.

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Section: Discussionmentioning

confidence: 99%

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Eisfelder,

Boemke,

Gessner

et al. 2024

Remote Sensing

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“…Accordingly, most low-income countries prioritize agriculture in their national development strategies and investment plans (see, e.g., Kamenya et al 2018 ; Makombe et al 2019 ; Chitiga et al 2020 ). However, agriculture is also particularly sensitive to climate variability, which is expected to increase under climate change (Omotoso et al 2023 ). Incorporating climate uncertainty into the development discourse is thus a research and policy priority.…”

Section: Introductionmentioning

confidence: 99%

Revisiting development strategy under climate uncertainty: case study of Malawi

Mukashov,

Thomas,

Thurlow

2024

Climatic Change

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This paper analyzes the effectiveness of agriculture-led versus non-agriculture-led development strategies under climate-induced economic uncertainty. Utilizing Malawi as a case study, we introduce the application of Stochastic Dominance (SD) analysis, a tool from decision analysis theory, and compare the two strategies in the context of weather/climate-associated economic uncertainty. Our findings suggest that an agriculture-led development strategy consistently surpasses its non-agriculture-led antagonist in poverty and undernourishment outcomes across almost all possible weather/climate scenarios. This underscores that, despite increasing exposure of the entire economy to weather/climate uncertainty, agriculture-led development remains the optimal strategy for Malawi to reduce poverty and undernourishment. The study also endorses the broader use of SD analysis in policy planning studies, promoting its potential to integrate risk and uncertainty into policymaking.

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“…Weather uncertainty is the most significant uncertainty in agriculture among all categories. Climatic events like droughts, floods, and extremely high or low temperatures are unpredictable weather patterns, and its severity can majorly impact livestock production and limited scale of agricultural production, underscoring the importance of weather in agriculture [15]. Weather conditions play a crucial role in determining the quality of crop output as it gets moved from the field to storage and then to the market [16].…”

Section: Introductionmentioning

confidence: 99%

Exploring Perceived Weather Uncertainty among Indian Farmers: Insights from Micro-level Analysis and Interpretations

Patra,

Acharya,

Haque

et al. 2024

Int. J.Environ. Clim. Change.

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India's economy relies heavily on farming, which is crucial for growth, jobs, and survival. However weather uncertainties like droughts, floods, and heatwaves make farming challenging. These uncertainties lower crop yield and quality, causing financial problems for farmers. Despite farmers' efforts, unpredictable weather still hurts crop management. Understanding how farmers feel about these uncertainties is vital for adapting to climate change and reducing risks. The present study was conducted in three villages: Bhomrapara, Mitrapur, and Maniktala of Haringhata block of Nadia district of West Bengal, India, from 2021 to 2022. Nadia district was purposively selected as it comes under the new alluvial zone (NAZ), which has decent productivity in terms of agriculture. A hundred farmers with good farming experiences were identified from the sampling frame, and responses were collected through a structured interview schedule. The study explores the diverse perspectives of Indian farmers on climate-related uncertainty, reveals that factors such as age, education, and landholding size significantly influence these views. Experienced farmers and those with more extensive landholdings perceive more significant uncertainty, with irrigation practices, crop yield, and cultivation costs play crucial roles. The study emphasizes the urgency of proactive risk reduction and resilience enhancement to avoid severe implications for agriculture and food security.

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Climate change and variability in sub-Saharan Africa: A systematic review of trends and impacts on agriculture

Cited by 52 publications

References 51 publications

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Revisiting development strategy under climate uncertainty: case study of Malawi

Exploring Perceived Weather Uncertainty among Indian Farmers: Insights from Micro-level Analysis and Interpretations

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