Environmental change daunts the aptitude to ensure global food security. The greatest significant threat to world fronting is how to mitigate environmental pollution without sacrificing agricultural productivity. The sharp rise in carbon dioxide emissions and global warming is altering the agriculture productivity's patterns as its vulnerability can be sensed by farmers. The spillover impact of climatic changes predicted to be great but differs by area and crop. This research fulfills the unprecedented need of adopting robustness methods to quantify the impact of CO2 emissions, agriculture labor, land, feeds, and fertilizers on agriculture productivity over five decades-long data structured in time-series. The fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), wavelet transform coherence (WTC) method, and gradual shift causality, tools have been espoused to observe the dynamic linkage in the long-term and short-run. The ARDL bound test confirms long-term co-integrated relation among the indicators. Furthermore, there is evidence of positive association between agriculture productivity and regressors which is also supported by the wavelet coherence outcomes. The Gradual shift causality test present a uni-directional causal link from all determinants to agriculture productivity which illustrates that all the regressors can significantly predict agriculture productivity. So, policies need to be formulated to explore a practical expansion approach with efficient use of fertilizers and feeds at an optimum level, in addition to environmental protection by encouraging public and private investment in agricultural research.