An estimated 70% to 80% of water resources are used for agricultural production. Irrigation helps maintain adequate soil moisture for crops; however, drought can impact both the amount of water required for production and crop yields. Different crops are affected by moisture conditions in different ways, as some can handle lower moisture conditions better than others. There are many drought indices that quantify low-moisture conditions, but they are not crop-specific and therefore do not quantify moisture stress for a given crop. The goal of this study was to evaluate a crop-specific drought index by determining the indexs ability to reflect yield trends due to moisture conditions. The drought index is a weekly index that uses five variables: precipitation, temperature, biomass production, soil moisture, and transpiration. This article presents a case study that examines the effectiveness of the crop-specific drought index in determining moisture stress to crops by comparing the drought index with annual yield values. The site chosen for this study was the upper Colorado River basin (UCRB) in west Texas because it is prone to drought. Cotton is one of the most widely grown row crops in this region and was therefore used in this study. A hydrologic and crop model, the Soil Water Assessment Tool (SWAT), was used to determine the biomass production, soil moisture, and transpiration. Observed precipitation and temperature data were also used both in the SWAT model and in the drought index. A multiple linear regression model was created for each week of the growing season because each variable is important during different weeks of the growing season. For example, in the UCRB, soil moisture was found to be more important during the beginning of the growing season, while biomass production was found to be more important during the end of the growing season. Ultimately, the drought index was found to be a good indicator of moisture-related yield conditions, with an R2 of 0.67. This index can be used to assess moisture stress to agricultural crops and aid in management decisions related to irrigation timing. Keywords: Crop modeling, Drought, Drought index, Hydrologic modeling, SWAT, Water conservation, Water management, Water stress.