Days suitable for field work (DSFW) is an important piece of data for production agriculture and agricultural extension focused on practical decision making about investment in farm machinery and cropping systems management. It is, however, noteworthy that there has been limited attention paid to DSFW. To fill this gap, this study tries to answer two research questions: (1) what is the trend in DSFW during the planting and harvest period from 1980-2010? (2) what is the accuracy of a predictive econometric model of DSFW based on agro-environmental data? To tackle the economic dimensions of DSFW, we model DSFW consistent with two major approaches in climate change impacts on agriculture: the Ricardian approach and the panel estimation approach. We first specify the regression model of DSFW in panel model from two conceptual approaches: the response function and the factor demand function of cost minimization. Both approaches provide consistent regression specification of fixed and random effects models. We construct an unbalanced panel of weekly DSFW observations, historic weather data, and soil data in five Corn Belt States for 1980-2010 at the Crop Reporting District (CRD) level to implement out-of-sample and in-sample prediction analysis. The results show that the random effects model is the most suitable model to perform climate change response analysis for our data. This paper contributes to the literature in three ways. First, the analytical derivation of two econometric interpretations of DSFW and link them to econometric model specification strategies are easily extended to other agro-environmental analysis. Second, the estimation results for panel models empirically demonstrate that random effects model could be proper model specification taking into account soil effects. Lastly, we discuss that DSFW could be an important constraints for policy corresponding to climate change and its adaptation.