A Spatial Analysis of Technology Adoption in Irrigation

1. A Spatial Analysis of Technology Adoption in Irrigation Andrew Wright Darren Hudson Maria Mutuc Sukant Misra

2. Introduction Over time irrigation technology has become more efficient in extracting water. More efficient technology leads to increased reliance on related inputs. Implication for irrigation: more efficient technology increases water use.

3. Irrigation Technology Adoption • Technology adoption is essentially a profit maximization problem. • Economic Factors • Large capital investment • Learning costs • Communication with others can reduce these costs. • Information spillovers may exist. • Physical factors are also important. • Implies that physical location is important to the adoption choice. • Physical and economic (communication) factors imply a spatial relationship to technology adoption.

4. Study Objective • Determine the importance of spatial relations on the adoption of center pivot technology. • Does location matter? • Do information spillovers occur? • The results can be used to better understand the adoption process of sub-surface drip technology.

5. Methods • Study area- High Plains Underground Water District n0. 1 (HPWD). • Exploratory analysis • Does spatial clustering exist? • Regression analysis • Estimate OLS model to identify spatial dependence. • Estimate spatial regression models based on OLS results.

6. Methods • Regression variables • Dependent variable- number of center pivots in a county. • 7 years of data collected by the HPWD. • Independent variables • Saturated thickness of the Ogallala Aquifer • Current year • 5 year change • Irrigated acres as a percent of total acres • Aridity • Distance to an experiment station

7. Methods OLS model ln(pivots) = β0 + β1ln(ST) + β2change + β3distance + β4%irr + β5aridity + Є Spatial Lag Model (SAR) ln(pivots) = β0 + ρWln(pivots) + β1ln(ST) + β2change + β3distance + β4%irr + β5aridity + Є Spatial Error Model (SEM) ln(pivots) = β0 + β1ln(ST) + β2change + β3distance + β4%irr + β5aridity + μ where μ = λWε + Є

8. Results Moran’s I values

9. Results Moran Scatter plot- 1986 Moran Scatter plot- 1990

10. Results Moran Scatter plot- 1993 Moran Scatter plot- 1995

11. Results Moran Scatter plot- 1998 Moran Scatter plot- 2005

12. Results Moran Scatter plot- 2008

13. Results • Summary of regressions • Performed 3 tests of spatial dependence • Moran’s I • Two Lagrange Multiplier (LM) tests • Existence of spatial autocorrelation (λ) • Existence of spatial dependence in the dependent variable (ρ) • Although Moran’s I values indicated statistically significant spatial autocorrelation in 1986, 1990, 1993, and 2008, LM tests did not confirm this. • Wald tests for λ = 0 in the SEM and ρ = 0 in SAR models found no statistical evidence of spatial dependence.

14. Conclusions • Location matters • No statistical evidence that information spillovers exist. • Implication for outreach

15. Further Research Decrease the level of aggregation using GIS data. Survey producers regarding irrigation choices. Do individuals react differently to physical factors based on location?

16. Thank You Questions

17. 1986 Results

18. 1990 Results

19. 1993 Results

20. 1995 – 2008 Results