1,720,999 research outputs found
Does incubating bagged soil cores prior to oven drying effect extractable nutrient concentrations?
No full textFALSEPublishe
A preliminary assessment of the ability of the DGT soil phosphorus test to predict pasture response in Australian pasture soils
No full textFALSEPublishedHobart, Australi
Improving nutrient management on dairy farms in north west Tasmania
No full textFALSEPublished onlineHobart, Australi
Decrease in environmental and agronomic phosphorus concentrations when fertiliser is reduced or omitted from a range of pasture soils with varying phosphorus status
No full textFALS
Proceedings of the 5th Joint Australian and New Zealand Soil Science Conference: Soil solutions for diverse landscapes
No full textFALSEPublishedAustrali
Management zone delineation in arable crop systems
No full textManagement 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
Assessing biomass yield of kale (Brassica oleracea var. acephala L.) fields using multi-spectral aerial photography
No full textAerial images were taken in June 2014 with a multispectral VIS/NIR camera of the canopy from 14 kale (Brassica oleracea var. acephala L.) fields in Canterbury, New Zealand before this forage was grazed by cows. Images were taken at 716m and at 1,372m flying altitude. Calculating the Green Normalised Difference Vegetation Index (GNDVI) from green and NIR channels proved to be the best representation of yield (dry matter) variation from manual biomass cuts in these fields. Several hundreds of individual images covering parts of the fields were semi-automatically stitched to composite images covering full blocks of fields.
Highest coefficients of variation (CV) of GNDVI values in a field are linked with low yield averages, often found at vary patchy fields (CVs of 20%). High yielding fields were less patchy and had CVs of less than 8%. A non-linear calibration curve was derived from the presented data. This functional relationship can explain 70% of the variance of the measured biomass yield data with reflection data of the canopy from these fields. This explaining power does not change when data from aerial images from higher altitudes were analysed. This independency of the preliminary model from height will allow using such an approach with standard high resolution cameras from various platforms (e.g. conventional aircraft, UAV/RPAS).
Grouping the GNDVI data also allows delineating zones of similar yield levels within the forage fields. Such zoning enables farmers to adapt fertilizer application to the yield expectation of such zones or to manage the feed provision for their grazing cows in a spatial variable way across and between fields. The zones can be used for directing the manual sampling of biomass in cases when farmers deem estimations of biomass yield from aerial imagery to be inaccurate. For all these steps higher resolutions - associated with lower flying altitudes - are necessary
Nitrogen fertiliser management with zone characterisation in grazed pasture systems
No full textSpatial information is frequently used for managing arable crops. The idea of developing management zones is often to enable accurate fertiliser supply for local crop needs. This helps avoid excessive introduction of nutrients, such as nitrogen, into the environment, and also to reduce fertiliser costs. Despite the success of this concept in arable farming, it is a poorly adopted practice for the management of grazed pastures.
Grazed pasture systems have an additional level of complexity compared to monoculture, annual crops. Pastures are typically perennial in nature with short intervals between harvests (by a grazing animal) and therefore require fertiliser applications to maintain biomass production. Additionally, pastures often consist of two or more desirable plant species and the distribution of waste from livestock results in many small patches of very high nutrient content.
We propose a concept to create management zones of grazed dairy pastures, using the spatial attributes of pasture paddocks. The target will be to identify zones of most likely high nitrogen availability and use this information to estimate the required local fertiliser target. The spatial information required for this approach may include: soil variation, irrigation, animal density, slope, farm infrastructure (i.e troughs and shelter) and previous pasture growth.
Using a geographical information system, the spatial information for an area can be utilised to create map layers. These layers can then be spatially related and zones for the application of varying amounts of fertiliser can be developed at the sub-paddock scale. We are in theprocess of deriving response curves for N-ramps on selected
paddocks in NZ and Australia which have sufficient spatial variability of the mentioned site characteristics.
We undertook a theoretical feasibility study to compare both uniform and variable nitrogen fertiliser application as an initial investigation of the potential benefit of zone management. The integrated result (value of feed –cost of fertiliser –cost of
environmental impact) of applying nitrogen variably across a paddock of dynamic soil using a non-linear response function was slightly lower than for uniform application. It is expected however, that increased understanding of spatial variables in pastures will increase the benefits of zone management
An assessment of the denitrification potential in shallow groundwaters of the Manawatu River catchment
No full textDenitrification in shallow groundwaters is an important nitrate attenuation process which is dependent on the characteristics of the contributing surface and subsurface environment. Little is known about the spatial variability and factors contributing to denitrification potential in shallow groundwater systems in the Manawatu River catchment. The objectives of the study, therefore, are (1) to determine the spatial variability of the denitrification potential in shallow groundwater, (2) to identify the factors affecting this denitrification potential, and (3) to quantify denitrification in selected sites in the catchment
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