Evaluating climate change impacts and seasonal dynamics in Senegal to predict crop yields and develop early warning signals - Panjwani, Shweta; Jampani, Mahesh; Amarnath, Giriraj; Sambou, Mame Henriette Astou (2024)
Food security has become a critical issue in Senegal due to agricultural losses from climate-related risks and the growing population. In recent years, several studies have reported crop yield losses as a result of seasonal climate variability and extreme events, but crop-wise in-depth analysis is lacking. In this context, we performed district-wise statistical and spatial extent analysis for major growing crops using earth observation and agronomic data from the government to estimate crop-wise correlation. Further, regression analysis was performed for major crops, such as maize and groundnut, using satellite-based climate and vegetation data and observed yield data over a 12-year period. Our results suggested that maize and groundnut crops are mainly distributed in all agroecological zones except the Niayes zone and Senegal River valley in terms of cultivated area and harvested crop yield. We found that seasonal rainfall, particularly from May to September, is highly correlated with the yield, and a 10-20% decrease in seasonal rainfall can lead to crop losses. Additionally, the impact of seasonal rainfall may differ across districts due to climate variability, the onset of monsoon, and cropping seasons. We used the best-fit combinations of rainfall and NDVI and machine-learning models to predict crop yield for the upcoming season for major crop growing districts, with an accuracy (R2) ranging from 0.6 to 0.8 and a one-month lag to the harvest period. The overall goal is to integrate the predictive modeling results into early warning systems such as CGIAR AWARE, which could enhance Senegal's agricultural resilience to climate change and inform decision-makers to take early action.