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Highlights Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased leading to a large increase in crop water productivity (CWP). Many factors have contributed to improved CWP, such as improvements in water delivery systems. Irrigation scheduling technologies have also contributed to improved CWP; however, farmer adoption of advanced scheduling technologies is still limited and there is significant room for improvement. Increased yields from improved cultivars without an increase in water requirements has also been important for CWP. Continued developments in sensor technologies and improved crop simulation models are two examples of future strategies that should allow the U.S. cotton industry to continue an upward trend in CWP. Abstract. Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased. Factors contributing to higher water productivity and decreased irrigation water use include migration of cotton out of the far western U.S. states to the east where more water requirements are met by rainfall; improved irrigation delivery systems with considerable variation in types and adoption rates across the U.S.; improved irrigation scheduling tools; improved genetics and knowledge of cotton physiology, and improved crop models that can help evaluate new irrigation strategies rapidly and inexpensively. The considerable progress over the last 40 years along with the promise of emerging technologies suggest that this progress will continue. Keywords: Cotton, Crop water productivity, Irrigation, Sustainability, Water use efficiency.

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Determination of an efficient number of testing locations in multiple-location tests for cotton (Gossypium hirsutum L.) fiber quality can allow removal of unnecessary locations while maintaining the statistical power in detection of genotype (g) by environment (e) interactions. Fiber quality data from Regional High-Quality (RHQ) tests from 2011 to 2016 were used to determine an efficient number of locations in the tests for fiber quality and relationships among locations for their representativeness and ability to discriminate among genotypes. Covariance parameters of g, location (l), and gl in the original RHQ tests were estimated in a random model. The simulating data with varying number of locations omitted from the original tests were created by performing 100 unique simulations. When locations were reduced to five, the standard deviations (std) of gl increased from 18 to 37% compared to the original tests. Further reduction of locations to four or less increased std of gl from 30 to 217% compared to the original tests. Therefore, five locations were determined to be an efficient number of locations in tests for fiber quality. The discriminating ability and representativeness of the eight locations for fiber properties were calculated as their distances to an "ideal environment", which was designed as a center in GGE biplot graphs for representativeness and discriminating ability. The relationships among locations were different across years. However, by averaging the distances across testing years, the locations of Stoneville, MS; Keiser, AR; Lubbock, TX; and College Station, TX were identified as the most representative testing sites for fiber properties.

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