Climate trends and soybean production since 1970 in Mississippi: Empirical evidence from ARDL model

Authors: SHARMA, RAMANDEEP - Mississippi State University; DHILLON, JAGMANDEEP - Mississippi State University; KUMAR, PUSHP - Indian Institute Of Technology; MULVANEY, MICHAEL - Mississippi State University; REED, VAUGHN - Mississippi State University; BHEEMANAHALLI, RAJU - Mississippi State University; COX, MICHAEL - Mississippi State University; KUKAL, MEETPAL - Pennsylvania State University; Reddy, Krishna

Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/11/2023
Publication Date: 9/13/2023
Citation: Sharma, R.K., Dhillon, J., Kumar, P., Mulvaney, M.J., Reed, V., Bheemanahalli, R., Cox, M.S., Kukal, M.S., Reddy, K.N. 2023. Climate trends and soybean production since 1970 in Mississippi: Empirical evidence from ARDL model. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2023.167046.
DOI: https://doi.org/10.1016/j.scitotenv.2023.167046

Interpretive Summary: Climate change poses a significant threat to agriculture. However, climatic trends and their impact on Mississippi soybean are unknown. Exploring how crops have been responding historically to weather conditions at a finer scale is essential for developing agricultural strategies for projected climate scenarios. Scientists from Mississippi State University, Mississippi State, Mississippi; Indian Institute of Technology, Odisha, India; Pennsylvania State University, State College, PA; and USDA-ARS, Crop Production Systems Research Unit, Stoneville, Mississippi have assessed the effect of previous climate change from 1970 to 2020 on soybean. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). A positive trend in Tmin and CO2, and a negative trend in DTR was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted. Soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18% over the long-term and by 5% over the short-term for every 1°C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7% and 3%, respectively. Overall, soybeans in MS exhibited variable sensitivity to short- and long-term climatic changes. These results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.

Technical Abstract: Exploring how crops have been responding historically to weather conditions at a finer scale is essential for developing agricultural strategies for projected climate scenarios, however, crop-climate estimations in Mississippi (MS) are elusive. Therefore, this research attempted to i) estimate climate trends between 1970-2020 in MS during the soybean growing season (SGS) using the Mann-Kendall and Sen slope method, ii) calculate the impact of climate change on soybean yield using an auto-regressive distributive lag (ARDL) econometric model, and iii) identify the most critical months from a crop-climate perspective by generating a correlation between the detrended yield and the monthly average for each climatic variable. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). All required diagnostic tests for pre-analysis (multicollinearity, unit root problem, ideal lag length selection, and cointegration check), post-analysis (heteroskedasticity, coefficients stability, and serial correlation), and model sensitivity were performed, and assumptions were fulfilled. The statistical criteria used to assess the models' goodness-of-fit included the fully modified ordinary least square (FMOLS) method, the cumulative sum (CUSUM) test, and the CUSUM square test. A positive trend in Tmin (+0.25°C/decade) and CO2 (+5.14 Mt/decade), and a negative trend in DTR (-0.18°C/decade) was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted as +0.11°C/decade, +3.03 mm/decade, and -0.06%/decade, respectively. Furthermore, soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18.11% over the long-term and by 5.51% over the short-term for every 1°C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7.76% and 3.04%, respectively. Altogether, soybeans in MS exhibited variable sensitivity to short- and long-terms climatic changes. The results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.