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| Researcher(s) | N/A |
|---|---|
| Contact email | admin@cfig.org.au |
| Contact phone | 0476046100 |
| Year(s) | 2016 |
| Contributor | Corrigin Farm Improvement Group |
| Trial location(s) |
Corrigin, WA
|
This project aims to evaluate if there is any difference in deriving management zones from soil or production spatial information and in what situations each of these layers may be useful to help maximise grower investment in PA technologies.
The relationship of the different layers varied across the case studies. The cause of yield variation commonly varied within production zones. EM and gamma can help interpret causes of yield variation. EM strongly correlated with yield in landscapes with highly contrasting soils i.e. sands to clays at Corrigin and Wickepin. Gamma helps delineate different soil types in combination with EM. These layers were used to determine variable ripping zones and gypsum. No layers were very useful on their own. Topsoil pH did not correlate with any data layer therefore grid sampling is recommended to accurately map pH.
This study investigates the use of spatial information to define within-paddock management zones in the Kwinana West zone. Results show zone management is not a ‘one size’ fits all approach. Analysis of paddock variability on three case study farms at Wickepin, Corrigin and Popanyinning showed that the cause of crop yield within a production zone can vary significantly. For example, two low performing areas can be low for different reasons such as an ironstone gravel or a potassium deficient sand and require different management, making ground-truthing essential. This makes zoning for fertiliser in these landscapes that have high variability, challenging. It is not a case of production vs soil zones. It is a combination of the information that will determine the best management option to maximise profitability. Farmer knowledge of the paddock also plays a significant role determining management zones. Electromagnetics (EM) and gamma radiometrics (Gamma Potassium, Thorium, Uranium & Total Count) can help interpret causes of yield variation. EM strongly correlated with yield in landscapes with highly contrasting soils (i.e. sands to clays at Corrigin and Wickepin). Gamma helps delineate different soil types in combination with EM, however no layers were particularly useful in isolation. Interpretation of the different gamma layers varied on a paddock by paddock basis. The development of management zones was considered for variable rate lime, potassium, gypsum and ripping. The defined zones were different for each management issue as were the layers of information that were helpful. For example, EM and Gamma Thorium (Th) can be used to identify ironstone areas for variable ripping and yield in one paddock correlated to soil potassium but not in other paddocks. Topsoil pH did not correlate well with yield, biomass, or EM which is likely due to the fact that surface pH is rarely the primary driver of yield variation, and more commonly it is the water holding capacity of the soil. Grid soil pH mapping of the topsoil is globally accepted as a more reliable method for developing accurate variable rate lime applications. Using precision agriculture technologies can be frustrating. There were problems with yield data collection at one farm due to a faulty yield monitor. More farmers should be collecting, storing and most importantly utilising yield data. It is an effective method for defining within paddock variability and a great entry point for zonal crop/soil management. Over 60% of farmers in Australia have a yield monitor (CSIRO, pers comm. 2012) yet few properly calibrate, store or examine the data after each season. Another important learning from this project is that using technology for paddock scouting, such as IPADs or IPHONES, was very challenging due to intermittent mobile data signal. Keep it simple! Collecting multiple spatial information layers can lead to data over load and difficulty making use of the data as there is so much information to digest. Start with a yield map and/or aerial photo, assess variation using local grower knowledge and strategic soil sampling. This process of utilising grower knowledge underpins the success of any precision agriculture plan as it focuses variable rate management strategies around the key limiting yield constraints for each paddock.
| Lead research organisation |
Corrigin Farm Improvement Group |
|---|---|
| Host research organisation |
Corrigin Farm Improvement Group |
| Trial funding source | GRDC FUT0001 |
| Related program | N/A |
| Acknowledgments |
Thank you very much to the case study growers Clinton Hemley, Steve Lyneham and Craig Larke and agronomists Hilary Wittwer and Angus Sellars. A huge thank you to the Facey Group and Corrigin Farm Improvement Group. The Precision Agriculture Pty Ltd staff Brett Coppard, Peta Neale, and Grant Canning. |
| Other trial partners | Not specified |
| Crop type | Cereal (Grain): Barley |
|---|---|
| Treatment type(s) |
|
| Trial type | Experimental |
| Trial design | Unknown |
| Sow date | Not specified |
|---|---|
| Harvest date | Not specified |
| Plot size | Not specified |
| Plot replication | Not specified |
| Other trial notes |
Three case study farms were selected at Wickepin, Popanyinning and Corrigin. Each grower selected two focus paddocks that had soil types typical of their farm and the area. Data layers collected included yield, biomass imagery (historical analysis), electromagnetics 0.5m and 1m, gamma radiometrics (Total counts, potassium, thorium, uranium), elevation (from the farm GPS systems), and aerial imagery. The layers were ground-truthed by soil sampling and farmer and agronomist knowledge. Zonal statistics were completed to determine correlations between datasets. Based on data interpretation zone manage applications investigated included variable ripping, lime application, potash and gypsum. |
SILO weather estimates sourced from https://www.longpaddock.qld.gov.au/silo/
Jeffrey, S.J., Carter, J.O., Moodie, K.B. and Beswick, A.R. (2001). Using spatial interpolation to construct a comprehensive archive of Australian climate data , Environmental Modelling and Software, Vol 16/4, pp 309-330. DOI: 10.1016/S1364-8152(01)00008-1.
Plant Breeders’ Rights (PBR)
Some crop varieties used in research trials may be covered by Plant Breeders' Rights (PBR). Users should confirm PBR status of relevant varieties by searching the IP Australia PBR database, http://pericles.ipaustralia.gov.au/pbr_db/ or Variety Central (http://varietycentral.com.au/) and contacting variety owners for further information if required. Where possible, varieties have been listed according to their registered PBR name, although OFT cannot and does not guarantee that the information is accurate or complete.
Note that PBR cover is administered through the Plant Breeders’ Right Act 1994 (