Exclusion zones for variable rate nitrogen fertilisation in grazed dairy pasture systems in New Zealand

To assess the variability of total soil nitrogen (TN) on grazed and irrigated pastures, TN was quantified from spatially distinct “areas” within the paddock (irrigated and non-irrigated areas, around the gates, and around the troughs) on two dairy farms located in Canterbury, New Zealand. During soil sampling, each area was sub-divided and multiple soil samples were taken to ensure adequate spatial representation of each area. The results showed there were no differences in TN between the farms, but differences were detected between the paddocks (P< 0.001), largely due to the significant interaction between the areas (gates and troughs) in different paddocks (P< 0.001). The greatest variability in TN was around the gates, due to either much higher or lower TN near the entrance of the gates. The TN levels returned to concentrations that were similar to those in the surrounding pasture after 4 m distance from the gates. This study shows while TN concentrations are relatively consistent spatially within pastures, there is high variability in TN in proximity to some farm infrastructure, such as gates and troughs

Contributions from engineering and science to measure, model and manage soils for Precision Agriculture in New Zealand

Contributions from engineering and science to measure, model and manage soils for Precision Agriculture in New Zealand. An outline of who Lincoln Agritech are and what they research as a subsidiary of Lincoln University. A description is given of Precision Agriculture research within Lincoln Agritech

Management zone delineation in arable crop systems

Management Zone Delineation (MZD) could be of increasing importance for its economic and environmental benefits through varying rates of crop inputs to meet site-specific demands across individual fields. However, research on MZD in arable cropping systems is limited in New Zealand. Previous work from Lincoln Agritech Ltd. presented the benefits of Precision Agriculture for large scale farmers and contractors using yield, soil and aerial images to adopt Variable Rate Application of seeds, fertilizer and agro-chemicals. Furthermore, improved irrigation efficiency in wheat and maize cropping systems and maize zone delineation using Active Light-Nitrogen sensor, NIR camera technology and SPAD chlorophyll meter, were studied. The objective of the presented project is to develop methods and tools to identify optimal N supply rate for maize production with the emphasis on management zones and site adapted plant population densities in New Zealand. Two rain-fed maize fields from Waikato and one irrigated field from Canterbury were selected for field experiments. Yield and elevation data in 2013 and 2014 collected with a John Deer 7050 Series Self-Propelled Forage Harvester. Data analysis and mapping of management zone were done in Esri, ArcGIS 10.2.2. software. VESPER 1.6 free version from Australian Centre for Precision Agriculture was used to interpolate the data. Three management zones with low, medium and high yield potential were derived in the final map. Satellite images from Google Earth Archive of previous years were also used to delineate field boundaries and management zones. A strip-plot experimental design with 3 replicates was allocated to each management zone at each field. Three treatment levels of N fertilizer: farmer’s best N-fertilization practice, +35% and -35% were applied using calculated N-fertilizer demand prescription maps with an 8-row VRA fertilizer spreader. We expect that soil electrical conductivity field survey data, compatibility of yield data and prescription maps between different software packages, and the inclusion of annual yield data would improve the discriminating power of the various approaches

Optical sensors for variable rate nitrogen application in dairy pastures

Reducing the amount of nitrogen (N) fertiliser applied to dairy pastures down to agronomically optimised levels would have positive economic and environmental results. The ability of commercially available optical sensors to estimate biomass yield and foliar-N uptake in pastures was investigated. Vegetative indices (Simple Ratio, SR; Water Index, WI; and Normalised Difference Vegetation Index, NDVI) from two active optical reflectance sensors (N-Sensor, Yara; and Greenseeker, Trimble) were compared with manually measured biomass and N-uptake in above-ground foliage. There were three measurements over time, from pastures that had received different N fertiliser applications rates (0, 10, 20, 40 and 80 kg N/ha). It was found that the sensors were able to detect differences in biomass and foliar N-uptake following defoliation of grazed pastures. The tested optical sensors have the potential to inform a real-time variable rate fertiliser application system

Urine patch detection using LiDAR technology to improve nitrogen use efficiency in grazed pastures

In grazed dairy pastures, the largest N source for both nitrate (NO₃⁻) leaching and nitrous oxide (N₂O) emissions is urine-N excreted by the animals. Additional application of N on urine patches as fertilizer may increase these losses. Identification of urine patches could reduce N losses in grazed pastures through more efficient fertilizer application and improved fertilizer N use efficiency (NUE). The aim of this study was to determine if remote sensing using Light Detection and Ranging (LiDAR) technology could accurately identify urine patches in grazed pastures based on height variation of the grass canopy in close proximity. Synthetic cow urine (7 g N L⁻¹) was applied to two blocks (20 m x 20 m) in a well-established pasture in Canterbury, New Zealand, which had no recent exposure to grazing animals or N fertilization. Urine patches were scanned weekly for five weeks. LiDAR based contour maps of the pasture were shown to accurately detect the asymmetric urine patches as well as calculate a percent area of urine based high N as early as one week after a simulated grazing event

Soil inorganic nitrogen in spatially distinct areas within a commercial dairy farm in Canterbury, New Zealand

For precision nitrogen (N) fertilisation of grazed dairy paddocks, soil N distribution needs to be quantified. It is expected that farm infrastructure will affect inorganic-N distribution due to its influence on cow grazing behaviour. Surface soil from four spatially distinct areas (main gate, water troughs, non irrigated and the remaining pasture) was analysed for soil ammonium-N (NH₄⁺-N) and nitrate-N (NO₃⁻ -N) from three paddocks (180 soil samples) on an irrigated commercial dairy farm in Canterbury, New Zealand. Variation between paddocks was higher for NO₃⁻ (P<0.001) than for NH₄⁺ (P=0.52). Differences between spatially distinct areas were detected for NH₄⁺ (P<0.001) but not for NO₃⁻(P=0.37), though there was variation in NO₃⁻ with distance from the gates and troughs. This study demonstrates methods for classifying spatially distinct areas of grazed pasture to quantify their influence on inorganic-N distribution. Further research is required to better understand variability