Using the EPA's Storage and Retrieval (STORET) database on water quality indicators at the county level for the 1993-2006 period, this study focuses on the relationship between broad types of land use and some of the main water quality indicators. It finds, among other things, that (a) land use intensity in agricultural activities (as captured by the values of crops and livestock production per unit of land area) positively and significantly influence the levels of total suspended solids, phosphorus, ammonia, and fecal coliform, while negatively affects the levels of cadmium and selenium, (2) urban land use (residential, commercial, and industrial) (as captured by population density) positively impacts the levels of arsenic, magnesium, chromium, fecal coliform but negatively affects the PH level, and (3) the levels of fecal coliform, nitrate, zinc, and copper rise with income per head up to a certain point and then tend to decline as income rises.

This study assesses the economic viability and implications for water and cropland allocations of adopting switchgrass in the Central Valley of California. It adapts the approach of Positive Mathematical Programming (PMP) to construct amulti-region, multi-input and multi-crop model of agricultural supply. The model is calibrated against both observed input allocations and the exogenous supply price elasticities. For each of the regions, the model derives the supply curve of switchgrass thereby indicating the extent and location of potential switchgrass production in California. The study finds that the regions in or near the Southern San Joaquin Valley corresponding to counties of Fresno, Kings, Tulare and the cluster of regions at the frontier of the Sacrament and San Joaquin Valley appear to be the early and dominant adopters. Sensitivity analyses show that the findings are quite robust to a range of reasonable changes in the underlying technological assumptions regarding both switchgrass and existing crops.

This study examines the relationships between water quality and socio-economic-demographic factors in California at the county level using 24 water quality indicators coming from seven different types of bodies of water. It estimates three classes of models: the traditional per capita income-pollution level specifications (the environmental Kuznets curve, EKC), a more inclusive model containing main socio-economic-demographic variables, and a model that includes these variables while accounting for spatial correlations too. For most water quality indicators, it does not find support for income-pollution specifications. For pollutants like phosphorous and total suspended solids, the level of agricultural activity is a significant determinant of water quality in California. However, for other surface water pollutants commonly considered agricultural pollutants, such as ammonia and nitrate, the level of agricultural activity is not statistically significant. Additionally, education, ethnic composition, age structure, and water area are all significantly correlated with various
indicators of water quality.