Benchmarking On-farm Irrigation Management in Nebraska

Transcription

1 May 2016 Benchmarking On-farm Irrigation Management in Nebraska Kate Gibson (née Boone), MSc student Department of Agronomy and Horticulture University of Nebraska - Lincoln

2 Background: Irrigation in Nebraska 94% of groundwater withdrawal in Nebraska is for irrigation 7.3 billion gpd (USGS 2014) Nebraska 1 st nationally in irrigated acres: 8.5 million irrigated acres (Maupin et al 2014) Important Nebraska Aquifers Need for understanding how we use water resources now and as climate change looms Total economic impact of irrigation in Nebraska is $3.6 billion/year (Thompson et al 2012) Maize accounts for 70% of irrigated crop land, soybeans for 19% UNL Conservation Survey Division, School of Natural Resources How to use water resources efficiently for crop production

3 The Problem Actual irrigation amounts have been unknown! High-quality irrigation data is hard to come by USDA: estimated state-wide irrigation total released every 5 years With limited data, limited conclusions How much do producers irrigate, why, and with what impact on yield?

4 Objectives Develop a framework to benchmark irrigation efficiency Identify opportunities to improve irrigation efficiency without hurting crop yields

5 Data Sources 534 corn and soybean fields over three years 2010, 2011, 2012 Maize area indicated in green Encompasses variety of soils and climates High-quality Irrigation for each individual field Management Practices Soil and slope Yield Dots identify surveyed fields

6 Determining Potential Irrigation and Yield Validated crop models to simulate irrigation requirements and potential yield for each field HybridMaize for maize simulations SoyWater model for soybean irrigation requirements, SoySim model for soybean yield potential Simulations account for field, soil, and weather data Compare applied irrigation and actual yield to field-specific irrigation requirements and potential yield Relative Irrigation = Actual Irrigation + In-season Rainfall Simulated Irrigation Requirement + In-season Rainfall Relative Yield = Actual Yield Simulated Yield Potential

7 Where are producers yield and irrigation outcomes? 73% of maize and 41% of soybean fields are over-irrigated! ~15% difference in average irrigation between C/D and A/B Four categories based on relative in-season water supply and yield: A, B, C, D Each datapoint represents a field-year A C A C Relative Yield B D % of fields A 15% B 12% C 42% D 31% B D % of fields A 28% B 32% C 18% D 23% Relative Irrigation

8 Causes of irrigation trends What determines whether a field is in category A/B or C/D? Relative yield R e la tiv e Y ie ld Near-optimal Irrigation A B Excess Irrigation C D Relative in-season water supply R e la tiv e I rrig a tio n

9 Role of soil in explaining irrigation inefficiencies Coarse-textured soil is not only irrigated more, but more than required! 1 mm more than required/unit available water holding capacity Excessive Each datapoint represents a field-year Near-optimal Course soil texture Fine soil texture

10 Irrigation scheduling methods and inefficiencies Non-specific scheduling methods resulted in the highest average irrigation difference irrigating 103 mm (~4-5 inches) more than recommended. A: Sensors, computer software B: Agronomist, probe C: Fixed schedule, crop visual inspection, neighbor, etc. I rrig a tio n d iffe re n c e (m m ) n = P -v a lu e < % 6 4 % 14 % Average irrigation difference (mm) % of total fields n=339 A B C I rrig a tio n s c h e d u lin g m e th o d

11 Impact of irrigation scheduling methods on yield However, no difference in relative yield between scheduling methods 1.0 n = P -v a lu e = Average relative yield A: Sensors, computer software B: Agronomist, probe C: Fixed schedule, crop visual inspection, neighbor, etc. R e la tiv e y ie ld % 6 4 % 14 % % of total fields n= A B C I rrig a tio n s c h e d u lin g m e th o d

12 Estimating Regional Irrigation Savings What would irrigation water savings be if those fields with in groups C and D were shifted to 1.0?

13 Estimating Regional Irrigation Savings Technology Extrapolation Domains (TEDs)* basis for up-scaling TEDs: spatial unit, specific Soil and weather considered relatively combination of climate & soil homogeneous within a TED 4 TEDs to represent study area Covers 43% of irrigated maize area and 52% of irrigated soybean area in Nebraska *Unpublished: TEDs under development by Juan Ignacio Rattalino Edreira and Nicolas Guilpart TEDs with irrigated crop area From Spatial Production Allocation Model (SPAM) Actual irrigation water volume for each crop, region, year (Reported irrigation for [Zone], [Year], [Crop]) x (Area of [Crop] in [Zone]) = Average water volume used Adjusted irrigation water volume for each crop, region, year (Adjusted irrigation for Zone], [Year], [Crop]) x (Area of [Crop] in [Zone]) = Average adjusted water volume SPAM Maize Area SPAM Soybean Area Actual water vol. Adjusted water vol. = Possible water savings Per a given crop, region, and year

14 Estimating Regional Irrigation Savings If over-irrigating fields reduced irrigation amounts to the field-specific simulated irrigation requirement, potential savings would be: 50% of current irrigation volume in NE in years with near- or aboveaverage rainfall 10% in drought years million m billion gallons million m billion gallons million m 3 77 billion gallons Not accounting for gravity-irrigated fields in Tri-Basin.

15 Conclusions Gap between current and optimal irrigation is exploitable without hurting yields à potential to save irrigation water Framework can be applied to any region with similar climate Can be used to track long-term implementation and effectiveness of irrigation technology and management practices

16 Acknowledgments Robert B. Daugherty Water for Food Institute Nebraska NRDs Nebraska Corn Board Nebraska Soybean Board Patricio Grassini and Grassini research group

17 Thank you!