Impact of environmental factors on pest population using multivariate cointegration model: evidence from India

One of the main challenges in improving agricultural productivity is the prevalence of pests and diseases, which are highly sensitive to extreme weather conditions. The interaction between weather patterns and pest infestations significantly affects crop yield. Traditional statistical methods often struggle to capture the complex temporal and geographical dynamics of these interactions. However, employing multivariate cointegration has proven valuable for estimating such interactions and quantifying the extent to which various environmental conditions influence pest populations. The study further investigated impulse response functions, which revealed substantial impacts of temperature and relative humidity on pest populations through unit standard deviation shocks to endogenous variables. Specifically, this research examined the dynamic causal relationships between major pest occurrences and environmental variables in 3 groundnut-growing states of India-Andhra Pradesh, Gujarat, and Tamil Nadu, using cointegration and Vector Error Correction Model techniques. The analysis incorporated key environmental variables, including temperature, relative humidity, and rainfall. Results from the Johansen test indicated a strong long-term equilibrium relationship between pest populations and climatic conditions, confirming the presence of at least one cointegrating vector at the 5% significance level. Granger causality tests further revealed that temperature and relative humidity had a unidirectional causal influence on the pest occurrence. Additionally, impulse response analysis further revealed that shocks to temperature and relative humidity produced significant and persistent effects on pest incidence over time.