Farming in sustainable ways is rapidly gaining importance, which necessitates a quantitative understanding of the major determinants of agricultural environmental consequences. The main objective of this chapter is to investigate the environmental effects and their drivers of agricultural production through the lens of fertilizer application and water quality. An integration of the econometric modeling and machine learning methodologies (multiple regression model and boosted regression trees method) was employed to dissect the relative importance and the non-linearity of each variable. The results showed that (1) household income level is a primary contributing factor to both fertilizers use and water quality, demonstrating an increasing marginal effect; (2) proximity factor-distance from provincial capital city positively correlated with these two terms; (3) water quality is more sensitive to natural conditions, such as precipitation and elevation, which also considerably affect fertilizer use; and (4) agricultural mechanization level is positively related to fertilizer use despite their non-linear effects. In summary, capital, technology, and labor input are the major socio-economic determinants of the environmental consequences, which were also largely associated with regional natural conditions. These findings provide quantitative insights that can be used to improve the agro-environment and achieve sustainable agricultural growth.