In 2017 our 15th Annual Conference focuses on automated tools for data collection, decision making and doing actual tasks on the farm (and beyond).
What do you want?
What’s on offer?
How will farms and management have to change?
We have a comprehensive programme. We’ve gone a bit outside the box to bring a variety including from outside the horticultural and arable sectors. We find cross-pollination and hybrid vigour valuable!
So register, come along and listen to excellent presenters, discuss the ideas with colleagues and go away with new understanding and plans.
Now in year two of our OnionsNZ SFF project, we have trials at the MicroFarm and monitoring sites at three commercial farms in Hawke’s Bay and three more in Pukekohe.
A summary of Year 1 is on our website. A key aspect was testing a range of sensors and camera systems for assessing crop size and variability. Because onions are like needles poking from the ground, all sensors struggled especially when plants were small. This is when we want to know about the developing crop, as it is the time we make decisions and apply management.
By November our sensing was more satisfactory. At this stage we captured satellite, UAV, smartphone and GreenSeeker data and created a series of maps.
We used the satellite image to create canopy maps and identify zones. We sampled within the zones at harvest, and used the raltioship between November canopy and February yield to create yield maps and profit maps.
We also developed relationships between photographs of ground cover, laboratory measurements of fresh weight and leaf area and the final crop yield.
In reviewing the season’s worth of MicroFarm plot measurements and noticed there were areas where yield reached its potential, areas where yield was limited by population (establishment), some where yield was limited by canopy growth (development) and some by both population and development.
This observation helped us form a concept of Management Action Zones, based on population and canopy development assessments.
Our aims for Year 2 are on the website. We set out to confirm the relationships we found in Year 1.
This required developing population expectations and determining estimates of canopy development as the season progressed, against which field measurement could be compared.
We had to select our “zones” before the crop got established as we did a lot of base line testing of the soil. So our zones were chosen based on paddock history and a fair bit of guess work. Really, we need to be able to identify zones within an establishing or developing crop, then determine what is going on so we can try to fix it as quickly as possible.
In previous seasons we experimented with smartphone cameras and image processing to assess canopy size and relate that to final yields. We are very pleased that photographs of sampling plots processed using the “Canopeo” app compare very well with Leaf Area Index again this season.
Through the season we tracked crop development in the plots and using plant counts and canopy cover assessments to try and separate the effects of population (establishment) and soil or other management factors.
We built a web calculator to do the maths, aiming for a tool any grower or agronomist can use to aid decision making. The web calculator was used to test our theories about yield prediction and management zones.
ASL Software updated the “CoverMap” smartphone application and we obtained consistent results from it. The app calculates canopy ground cover and logs data against GPS position in real time. Because we have confidence that ground cover from image processing is closely related to Leaf Area Index we are working to turn our maps into predictions of final yields.
The current season’s MicroFarm crop is certainly variable. Some is deliberate: we sat the irrigator over some areas after planting to simulate heavy rain events, and we have a poorly irrigated strip. We know some relates to different soil and cover crop histories.
But some differences are unexpected and so far reasons unexplained.
Together with Plant and Food Research we have been taking additional soil samples to try and uncover the causes of patchiness.
We’ve determined one factor is our artificial rain storm, some crop loss is probably runoff from that and some is historic compaction. We’ve even identified where a shift in our GPS AB line has left 300mm strips of low production where plants are on last year’s wheel tracks!
But there is a long way to go before this tricky crop gives up its secrets.
The Arawhata Catchment Integrated Storm Water Management project is drawing to a close, the majority of work is done but farm follow-ups continue. The aim of the project was to reduce crop loss from ponding and minimise erosion of soil to Lake Horowhenua.
We completed OptiSurface drainage analyses for 26 Levin properties covering 450ha of intensive vegetable cropping. OptiSurface calculates flood patterns and erosion risk and creates cut & fill maps for GPS levelling. An example is shown in our earlier post “Mapping for Drainage”.
Drainage and Erosion Management Plans were developed for each block. The plans identify drainage problem areas and erosion risks and recommend management strategies to respond.
Individual farms have done significant work to prevent erosion and reduce crop damage. Farmer actions to reduce sediment runoff and ponding include realigning bed direction, levelling, grassed headlands and drains and swales and sediment traps.
Stages in headland redevelopment
Now farms are required to have consent in this catchment, the Drainage and Erosion Management Plans are a useful component of the overall Farm Nutrient Management Plans required.
The crop at the MicroFarm is showing increasing variability. The cause of some is understood, essentially excessive water pre-germination. But in some poor performing areas the causes have yet to be determined.
The effect of our artificially applied rain event pre-emergence is clearly evident in late November.
However, we also see other areas that have poor crop development that are outside the area irrigated to create the artificial rain event.
Sharp differences in crop growth are evident in the new onion ground. Some parts that were heavily irrigated to simulate heavy rain show reasonable development. Areas that were not irrigated also show good development, but in some patches total crop loss.
Investigations of soil physical properties in these different areas are underway.
Most people will have heard about UAV’s or drones (officially RPAS) and many know of the existence of regulations here in New Zealand, but few know exactly what the rules are and who they apply to.
Two websites offer particularly helpful information for users of UAVs or RPAS:
This is the dedicated webpage of Civil Aviation, the controlling authority. Information about Parts to Civil Aviation Rules that relate directly to RPAS are:
Part 101 Gyrogliders and Parasails, Unmanned Aircraft (including Balloons), Kites, and Rockets – Operating Rules, and
Part 102 Unmanned Aircraft Operator Certification.
Operators of RPAS also need to be aware of other rules that affect them, for example Part 91 General Operating and Flight Rules.
Airshare acts as a UAV hub for New Zealand. It has information including how to operate your drone safely, plan all your UAV flights, and request access to controlled airspace.
You can find maps on the site showing where you can and cannot fly your UAV
NOTE The information contained on Airshare is not to be relied on as a substitute for a comprehensive knowledge of the relevant rules and regulations that apply to the operation of UAVs. It is the UAV operator’s responsibility to read, understand and operate any UAVs in accordance with the Civil Aviation Rules.
The industrial revolution gave us machines and agri-inputs that enabled us to farm at scale and speed. The green revolution began to unlock the potential of plant genes to increase yield. Now the digital revolution provides us with an opportunity to harness the power of ‘big data’ and technological innovation to radically re-engineer our horticultural production methods and supply chains.
Digitally informed decisions during production, harvesting, sorting, packing, storage and transit could be the basis for a step change to high profitability, high resource efficiency and low footprint horticultural value chains.
Identifying the research priorities that we need to realise this opportunity in New Zealand is a challenge in itself, given the pace of developments in sensing technology, robotics and the internet of things globally. Accordingly, Plant & Food Research assembled an expert panel from across its science teams, augmented with other specialists from New Zealand and Australia, to develop a digital horticulture research strategy.
The panel has taken a value chain approach to identifying research priorities, particularly in relation to production, harvesting, sorting and packaging, storage and transit. Future science needs are structured around the concepts of ‘sense, think, act’ for each part of the value chain and are linked by an ‘artery’ of data to feed forwards and backwards along the value chain.
Plant & Food Research looks forward to working with a wide range of partners to deliver this digital horticulture strategy for the benefit of New Zealand’s producers and exporters.
As part of the Onions NZ project “Benchmarking Variability in Onion Crops” a process was developed to generate yield and profit maps. This presentation explains the process using the example of a 7.3 ha paddock in Hawke’s Bay.
Data from a satellite image captured in late November were used to identify high, medium and low biomass zones. Paddock yield samples were taken from these zones at harvest and used to generate a paddock yield map. The average yield of the paddock was estimated at 95 t/ha, with a predicted total field harvest of 669 tonnes. This compares to the grower recorded harvest of 614 tonnes.
The relative yield data were combined with grower supplied costs and returns to determine gross margins across the paddock. Data were mapped in ArcGIS and a Gross Margin map with five “profit bands” produced. The highest band had a mean Gross Margin of $11,884/ha compared to the lowest at $3,225/ha.
The breakeven gross margin yield is estimated to be 62.5 t/ha at current costs and prices. The estimated cost to business of low performing areas is $27,945, assuming the whole paddock could achieve the top band mean yield.
The poorest performing areas were identified by the grower as impacted by a failed council drain and areas of slowed drainage in the main paddock areas. An OptiSurface® assessment using historic HBRC LiDAR elevation data analysed of the impact of ponding on the site and also suggested ponding was a significant issue.
An OptiSurface® landform assessment was conducted using both single plain and optimised surface designs and the soil movement required to allow effective surface drainage was determined.
The assessment showed ponding could be avoided by land shaping with 224 m3/ha soil movement and few areas requiring more than 100 mm cut or fill. The cost is estimated at $2,000/ha or approximately $14,000 total.
Minimising nutrient losses from cropping systems makes good financial sense. It also minimises any adverse impacts on our waterways, which is increasingly important in many regions as new national water policy requirements are implemented.
A common theme in many regions is the requirement that growers should, as a minimum, be managing nutrients according to agreed good management practices. However, there is relatively little long-term measurement of how good management practices throughout New Zealand impact losses of nitrogen (N) and phosphorus (P) from cropping paddocks.
To help fill this gap a network of permanent drainage fluxmeters has been established in commercial fields in the Canterbury, Manawatu, Hawke’s Bay, Waikato and Auckland regions over the last 18 months. There are a total of 12 sites in the network, covering a broad range of cropping systems, soil types, climatic conditions and management practices.
At each site fluxmeters have been installed at a depth of 1 m. Any water from rainfall or irrigation events that drains to 1 m is captured by the fluxmeters. It is then pumped to the surface and analysed for nutrient concentrations. Net losses can be estimated by combining these measured concentrations and measured drainage volumes.
Preliminary results from the network have highlighted a wide range in N and P losses in drainage water. Many of the losses have been comparatively low to date, evidence that economic and environmental risks can be successfully balanced through the integration of good management practices.
Where high losses have been observed this has resulted from large drainage losses and high nutrient concentrations in the drainage water.
Importantly, this is a long-term initiative and the value of the information from the network will increase over time as growers and regional authorities consider long-term trends.
Rob and his wife, Eliza, are now the fourth generation farming Greenvale, which was traditionally, a 100% dryland, prime lamb operation. It is now a diversified farming business with an extensive cropping program and a small breeding flock of crossbred ewes which are run alongside a lamb trading operation.
The farm has a long term average rainfall of 680mm and is now 60% covered with fixed pivot irrigators, reducing the risks of dry seasons. Soil types range from very heavy black canola running up to lighter sandy loams.
Over recent years, extensive development work has been put into practice. Technology has been implemented into the farming system to gain efficiencies in production and labour, such as livestock handling equipment, variable rate irrigation, Fieldnet, RTK guidance NDVI images underground drainage and grid soil mapping.
The introduction of PA has been implemented over a decade but in recent years the adoption of VRI and NDVI has taken this to a new level. It dramatically altered the way we view our crop management and has opened up many opportunities to increase production but at the same time reduce inputs
The operation now has a well-balanced irrigation system complementing the cropping and lamb production, allowing turn off lambs all year round.
In the 2012-13 growing season the Plant and Food researchers surveyed commercial potato crops in Canterbury and confirmed grower concerns that a “yield plateau” of approximately 60 t/ha was common. At this level, potato growing is becoming uneconomic.
Plant and Food Research computer-based modelling shows that yields of 90 t/ha (paid yield) are theoretically possible in the surveyed paddocks in most years. This shows a “yield gap” of about 30 t/ha.
The most important factors found to be reducing yield were soil compaction, the soil-borne diseases Rhizoctonia stem canker and Spongospora root galls.
Using CORE funding, Sarah and colleagues have been running a number of related trials, comparing field performance with modeled potential growth rates. They’ve used DNA to assess soil pathogens, applied a range of treatments and measured disease incidence and yields. They have also looked at the role of seed quality in potato emergence, variability and yield.
But it is not all about diseases. Soil compaction, structure and related issues such as aeration, drainage and water-holding show up as crop limiting factors. Also implicated are irrigation management and weeds.
Potatoes NZ reports that the use of guidance technology and variable rate application based on soil testing is being undertaken but there is limited crop based management of inputs. There may be opportunity to manipulate some inputs.
In paddock variability can be relatively easily identified using remote sensing equipment (both NDVI and Infrared) but there are three major problems with potatoes which are:
Remote sensing can identify differences in a paddock but these need to be ground truthed to determine what the reason for the difference is – e.g. canopy disease etc.
Often by the time a difference is apparent on a crop sensor map, even when it is ground truthed, growers cannot implement a management decision that will change the crop performance.
Yield maps are generally used as the baseline reference for Precision Agriculture and this is difficult and expensive to implement for potatoes.