1. Bajwa NCERA 180 Meeting, 23-25 March 2011, Little Rock, Arkansas 1 Precision Agriculture in Arkansas Sreekala G. Bajwa Associate Professor, Dept of Biological & Agricultural Engineering, University of Arkansas Division of Agriculture, Fayetteville Dharmendra Saraswat, Subodh Kulkarni, Leo Espinoza, Terry Griffin University of Arkansas -Division of Agriculture, Little Rock

2. Bajwa2 At a Glance Overview of Arkansas Agriculture Current and past PA projects Current & Future Issues and needs

3. Arkansas Agriculture CropAcres Planted (×1000) Acres Harvested (×1000) Production (tons)Value of Production (million $$) Rice 1791  178552.5 million 1330  Soybean 3190  31503 million 1245  Cotton545540257 K 395.9  Corn 390  3801.45 million 267.9  Hay114802.68 million 200  Wheat 200  150220 K 42  G.Sorghum403568.5 K 11.2  Bajwa NCERA-180, 2011 3 Agriculture sector accounts for 12% of Gross State Product

4. Farm Characteristics (USDA-NASS) Farm CharacteristicsYear 1997Year 2007 Average Farm size (acres)300281 Farms size  1000 acres, % 6.86.5 Farms size  99 acres, % 44.554.3 Farm sale < $9,9995759.6 Farm sale  $250,000 13.713.1 (91% sales) Farm sale  $500,000 6.69.3 (81.6% sales) Average operator age53.456.5 Farm land in conservation, acres188,902441,655 NCERA1804 Total land: 33.29 million acres Total farm land 13.87 million acres Total Population: 2.9 million Gandonou et al (2001): 1060 ac to purchase PA equipment 1350 ac in AR (Popp & Griffin, 2000)

5. 5 Precision Agriculture Adoption No comprehensive data available on PA adoption in Arkansas Arkansas lags behind other regions in PA adoption Most popular technologies –Yield monitoring –Soil grid sampling & zone management –Variable rate application –Remote Sensing –On-the-go sensing Popp and Griffin (2000); Groves et al. (2006); Torbett et al (2008); Winstead et al (2010)

6. Summary of Precision Agricultural Projects in Arkansas 6

7. 7 Precision Agriculture Projects: Remote Sensing Optical remote Sensing of plant response to stressors –N stress in rice and cotton –Water stress in cotton –Compaction in cotton fields –Diseases in soybean Soybean Cyst Nematode Sudden Death Syndrome & interaction with water stress Charcoal rot & interaction with water stress For early detection of stresses For site specific management Bajwa, Rupe, Kulkarni, Norman, Mozaffari, Vories, Huitink

8. 8 Soybean Diseases: SCN & SDS Project Bajwa, Kulkarni, Rupe Both SCN and SDS are Soil-borne pathogens, difficult to detect SCN is a major cause of yield loss ($1.69 billion in the US in 1998) SCN symptoms are similar to water/nutrient stress, and hence difficult to detect SCN and SDS interact

9. 9 Soybean Diseases: SCN & SDS Project To detect and map SCN and SDS incidence Several experiments – microplot, field strip plot with cutlivars, field plots with irrigation treatments Microplot experiment –4 cultivars: Control (SCN & SDS resistant), SCN resistant, SDS resistant, SCN & SDS susceptible –4 disease treatments: Control, SCN, SDS, SCN & SDS –2 years, 1 location

10. 10 Soybean diseases: SCN & SDS SDS susceptible SDS & SCN susceptible SDS & SCN ResistantSCN Susceptible Found differences in chlorophyll content between infested and healthy plants

11. 11 Soybean diseases: SCN & SDS There were differences in reflectance between infested and non-infested plants over time ControlSCN SDSSCN_SDS

12. Correlation with Canopy Reflectance Difficulty in getting plants infested Some cross-contamination Lack of good means of measuring infestation levels –Presence of pathogen does not mean infestation Confounding environment 12

13. Research Problem: To investigate cultivar, drought effects, and charcoal rot response on soybean canopy reflectance (ASD spectro-radiometer and CropCircle TM ACS-470) To develop a method to detect and map charcoal rot Soybean Charcoal Rot Study Doubledee, Rupe, Kulkarni, Bajwa

14. Background Information: 38M bu. lost/year Prevalent in heat and drought stressed areas Irrigated soybeans exhibit charcoal rot at critical plant stages after flowering begins Disease symptoms depends on plant’s growth stage at the time of infestation

15. Research Experiment: 2 disease treatments (inoculated and not inoculated), 2 water regimes (irrigated and water stressed), and 5 replications 4 soybean cultivars DT-97-4290 (moderately resistant), DP-4546 (moderately resistant), R-01-581FCR (drought tolerant), and LS-980358 (susceptible) Crop Circle TM ACS-470, ASD spectro-radiometer

17. Results : CropCircle: GNDVI, NDVI, VI= f(infestation) ASD spectra: 12 vegetation indices were tested Practical Application: Sensors detected charcoal rot before physical symptoms were observed. However, this was not consistent at all times during the growth season Infested plants had higher vegetation indices (CWSI NDVI, REIP, WI, D-Chl-ab, SAVI and SIPI) than non-infested plants at certain times during the season

18. Introduction-pH Variable Rate Liming Saraswat, Espinoza, Kulkarni, Griffin

19. Cost of Lime in AR $20/ton $25/ton $30/ton $35/ton $45/ton $10/ton

20. Variable Rate Liming Lime recommendation based on 2.5 ac grid soil sampling results Lime recommendation based on MSP sensor data Cost of Uniform Liming (recommendation 2 t/ac lime), @$25/ton = approx. 66*25*2 = $ 3300 Cost of variable rate liming, @$25/ton = 1.5 * 8 * 25 = $300 Savings = approx. $3000

21. VRT Components John Deere 6230 Tractor Barron Brothers International Grasshopper High Clearance Spreader Two 24 inch spinner disk 21 inch Conveyer Chain

22. VRT Components

27. Field Methodology  Target rate of 300 lbs over 11 pans across a 40 ft swath to determine swath distribution and applied amount  Pulse rate of 1500 on DJ360 rate controller for 3” gate height at spinner rpm of 500 provided the closest match  Lime density: 83 lbs/cu ft  Travel speed: 6 mph

28. Field Methodology  21 pans for each rate zone Pans within a row were 9.5 ft apart

29. Preliminary Results Similar results when transitions from 600 lb to 300 lb, 600 lb to 900 lb, and 900 lb- 600 lb were tested

30. Summary  A variable rate spreader system for lime application was put together  Missing parts and faulty part operation caused confusion  Manufacturer suggested procedure was revised to calibrate the spreader  Over application in the lower distribution and under application at higher distribution setting was observed  The spreader is under further evaluation

31. Current/Future Issues Water quantity and quality –Mississippi Alluvial Aquifer Drying at 15 cm/yr –Arkansas 5 th in irrigated acreage and second in percentage of crop area irrigated (Census 2007), with ~ 94% of ground water used for irrigation in Arkansas (USGS, 2005) –Low aquifer recharge rate of 2 cm/yr Climate Change –Climate adaptation and mitigation –Water availability and quality –Pest and disease incidence Energy - Fuel prices 31

32. Some of the Current Issues Raised by Growers Soil grid sampling – Value of grid sampling? what is the right grid size? Pest detection and site-specific management Data management and information extraction Challenges with equipment Getting the most out of precision agriculture 32

33. Special Thanks to.. Cotton Incorporated Cotton Foundation United Soybean Board Corn and Grain Sorghum Promotion Board Deano Traywick, Paul Ballantyne, and M. Ismanov, Dr. John Fulton, Auburn University Brian Mathis, TeeJet Engineer ACKNOWLEDGEMENT