3-Year Results of Total Farm Management with Precision Ag Technologies

1. 3-Year Results of Total Farm Management with Precision Ag Technologies Sharp T., Evans G., and Salvador A. Jackson State Community College – Jackson Tennessee Results The Objective of This Study: Compare Variable Rate Crop Input Application (VR) to Conventional Uniform Rate Application (CV) of crop inputs for physical and economic impact of the two systems. Introduction Remote sensing can provide rapid data acquisition over large areas. Airborne video and digital imaging systems have been used as a tool for assessing natural resources and monitoring agricultural crops since the 1980s. This is due to their low cost, high spatial resolution, real-time or near-real-time availability of imagery, and their ability to obtain spectral data in very narrow spectral bands. According to Diker et al. (2001), these data sets can be used to monitor temporal changes in crops, to detect abnormalities in the field, or to estimate the final crop yield. The purpose of the present work is to study the relationships among NDVI (Normalized Differential Vegetation Index), plant physical measurements, and crop input cost in eight cotton fields managed by conventional and variable rate systems. NDVI Zones reflect in NDVI from Low to Medium to High in line with Plant Productivity. VR and CV follow different season long curves for each productivity zone. The season long curve for the CV high productivity zone is too high, indicated by its reduced yield compared to the CV-M, VR-H, and VR-M yield in 2003. Impact on Boll Production For both 1st position bolls and total bolls, boll number is higher with VR than where CV management was applied Total Bolls 1st Position Bolls Power Index (PI) PI is a new index to integrate a Measurement for NDVI, HT and Nodes. Note the 9-5-03 date: With VR, each zone expresses a similar PI value. This indicates a similar maturity date with successful once-over harvest. In contrast, the wide separation found with CV resulted in a wide difference in maturity, different harvest dates, and two-pass harvest. Materials and Methods This study was conducted during the years 2001, 2002 and 2003. Four paired farm fields were used. Each one of these farms has two fields where this work was done. The areas of the fields vary from 15 to 33 acres each. Each field was classed into three productivity zones called low (L), medium (M), and high (H). These zones were classed via SSToolbox software using an unsupervised classification procedure. For each farm, one of the paired farm fields was Managed VR and the other field was Managed CV. The Variable Rate (VR) applications included lime, pre-plant fertilizer, seeding, in-furrow fungicide, in-furrow insecticide, plant growth regulator, in-season insecticides, in-season nitrogen, and crop termination treatments. The field data samples (final plants maps, weekly NDVI and weekly COTMAN input) were collected using standard total plant map methodology (Tugwell et al., 1998). The data was collected at eight sample points, per each zone, per field: low, medium and high NDVI based productivity zones. Each point sample had ten plants where the data was collected. The physical measurements sampled at each field point were: Stand Count, First Position Retention, Height (HT), Total Nodes Total Bolls, NDVI and Yield. The cotton was harvested by a cotton picker equipped with an AgLeader Yield Monitor, Model PF 3000. Yield data was processed and exported with AgLeader software (SMS). Yield data was then imported into SSToolbox for further processing and interpolation. Weekly observations were made with both equipment mounted and hand carried Green- Seeker imaging systems in 2003. End of season aircraft imagery was obtained for years 2000, 2001, 2002 and 2003. Best zone identification was observed to be after 750 HU-NAWF. Treatments were arranged in a split plot design with eight replications. All data analysis was performed using the General Linear Model Procedure in the SAS statistical software. The significance standard applied was Tukey’s Studentized 5%. CV VR Zone Stability 2001 imagery was used to plan 2002 and 2003 VR missions. Zone stability for each of 2001, 2002, and 2003 was compared. From the 2001 image, 81% of the zone area was correctly identified in 2002 and 2003 The pixel locations incorrectly mapped were located in the transition space between main Zones. The pixels located between L and M or between M and H could be incorrect, but only 7% were not correct for H to L.

2. 2001 Base Image All cotton in 2001 was managed as CV. This image will be classed into zones for future years work. Shot on 08-02-01 Prior to classing, a continuum of color from bright green to purple represents high productivity to low productivity. Optimum date for zone expression is After 750 NAWF. 2001 Control to Treatment 2001 Yield for All Fields In 2001 all fields and zones were compared for difference in yield potential. Control (CV) zones and fields were compared to Treatment (VR) zones and fields to evaluate if all were comparable. It was found that the low and high zones for all fields were the same. Yield in the control fields medium zone was 50# lint yield more productive than was the treatment fields. For this reason the yield comparisons are directly comparable for later years. All Years Control (CV) Compared by Zone for Yield by Zone 2002 First year of VR This is the appearance of the field after VR has been utilized. Notice that the entire field is much more uniform. For this reason, a manager can no longer use new imagery to create management zones. The application of VR technology allows the zones to better express crop productivity. Image shot on 08-08-02 Control (CV) Yield for All Years Yields for all zones were compared for all years. In 2001 (dry year) all yield was directly related to NDVI productivity values. In 2002 (average year) low zones were low yield but medium zones yielded higher than the high productivity zone but 2003 was below 2002. In 2003 (excellent year) low zones yielded 50# more than 2001 and 2002 low zones but medium zones were again the highest yielding zone in the fields. For two years out of three, 2002 and 2003, the high vigor zone exhibited lower yields than the medium productivity zones. High zone yield was 233# per acre below the high zone in 2002. 2003 high zone yields were 216#/A below the 2002 medium zone and 142#/A below the 2003 medium zone. 2002 Yields 2003 Yields VR Yields Compared to Conventional Yields for 2002 and 2003 In 2002 a strictly conservative low cost strategy was followed. Under this plan, cost were $80 per acre lower. Average yield was no different between the VR cropping system and the conventional system. In 2003 a more aggressive high yield strategy was utilized. This resulted in yield improvement in all zones of 53# low zone, 101# medium zone and 278# in the high zone with an average overall improvement for VR of 144# lint yield. Due to the various input changes from 2002 to 2003, the reduced cost for the VR crop producing system was still $80 per acre. VR Planning The 2001 image is classed into three zones. These zones will be used to make the VR application plans. These classed maps from 2001 data will be used for all future years. Different maps will be made for different inputs such as Nitrogen, Pix, Insecticides, Seeding Rate, In-furrow treatments and Defoliation. Low ns ns +101 +278 +40 +53 High Medium VR Management strategy was strictly following least cost VR Management strategy was for maximum yield and least cost • Conclusions • The use of variable rate technologies for cotton production is economical, practical and profitable. • Reduced crop inputs, allocated correctly for productivity zones, have resulted in crop input savings of $75 to $90 per acre (2002 and 2003) when VR is compared to CV cotton production. No negative crop physical reactions for factors related to yield was observed in any year. In short, spend less money and make more cotton. • High-vigor Productivity Zones, as observed in cotton fields, are yielding below the expected potential in average to good productivity years. Correct yields were observed in very dry years only. • CV cotton production often results in reduced yields in High Productivity Zones. VR technologies can be used to correct this problem. • Crop productivity zones are stable temporally and spatially with 81% of a field mapped correctly from a single year’s image (2001) and projected over future years (2002 and 2003). • One image of a cotton field, obtained after 750 Heat Units after NAWF, can be used to prepare VR application missions for all future crop production years. • Once VR crop production begins, images will not correctly express zone identity due to crop response to VR management changes and changing crop inputs. • Multispectral Imagery obtained from aircraft was found to be a • fully practical and successful tool to map and identify zones for VR • cotton production. • GreenSeeker has proven successful in weekly measurement of NDVI • exhibiting highly consistent measurements from different times and • locations. • Equipment mounted GreenSeeker .shp files were found to be practical • for zone identification similar to an aircraft image. • Both the Hand Carried and Equipment Mounted versions of Green- • Seeker should find successful uses by growers and consultants. Acknowledgements: National Science Foundation National Cotton Council Cotton Incorporated N-Tech Industries Oklahoma State University SST Development Group (SST Toolbox) GPS Inc. (Johnny Williams)