Ecological Indicators (Sep 2024)
Modelling the climate change and cotton yield relationship in Mississippi: Autoregressive distributed lag approach
Abstract
Development of mitigation strategies to combat climate change necessitates an advanced analysis of the historical connection between crops and climate. Such an analysis is lacking for the cotton (Gossypium hirsutum L.)-climate research in Mississippi (MS). Hitherto, research has been confined to small-scale experimental settings, leaving an opportunity to explore large-scale inferences. Therefore, the present study aimed to compute MS climatic trends during the cotton growing period (CGP) from 1970 to 2020 using the Mann-Kendall and Sen slope methods. The impact of climate change on MS cotton yield was assessed using the autoregressive distributed lag (ARDL) model. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PR), and CO2 emissions (COE). A required series of statistical tests, including pre- and post-analysis, model robustness, and goodness-of-fit were performed, and data met all criteria. Results revealed that Tmin (79.6 %) contributed more than Tmax (20.4 %) to the MS-climate warming over CGP. From 1970 to 2020, the Tmax, Tmin, DTR, and PR changed by + 0.30 °C, +1.17 °C, −1.07 °C, and + 22.54 mm, respectively, exhibiting change rate per decade of + 0.06 °C, +0.23 °C, −0.21 °C, and + 4.42 mm, respectively. Precipitation had no effect on cotton yield in the long or short-term. However, cotton yield significantly decreased with a rise in Tmax, and increased with a rise in Tmin and COE in the long-term. Conclusively, a 1 °C increase in Tmax reduced cotton yield by 6.1 %, a 1 °C increase in Tmin improved it by 5.5 %, and a unit increase in COE increased it by 0.45 % over the long run. Overall, the crop-climate link in MS cotton marked a varied sensitivity towards short and long-term, indicating the need to reassess current mitigation strategies. Additionally, testing the best agronomic practices in a controlled environment at the actual rates of climate change identified by the current study could provide cotton stakeholders with more precise and valuable insights.
