Category Archives: Plant Health

Field day – mesh crop covers for insect and blight control on potatoes

Tuesday 14 March 9.00 am – 11.00 am

FAR field site, North West corner of Springs and Ellesmere Junction Roads, Lincoln Google map.  Access off Springs Road, 300 m north of Roundabout.

Join FAR, Potatoes NZ, and the BHU Future Farming Centre for a roundup of results to date on the use of mesh crop covers for potato pest & disease control and the findings from the current field trial. 

  • How mesh covers are controlling blight
  • Mesh and tomato potato psyllid TPP control
  • Aphids and mesh
  • Potential yield boost from mesh due to improved microclimate

Get reports from the first two years trials here

Tomato potato psyllid (TPP) (Bactericera cockerelli) arrived in New Zealand in 2006 and has proved to be a important pest in a number of solanaceae crops, including potatoes.  While insecticides have proved effective for its management, this has caused a large increase in agrichemical use which is undesirable, and this option is not available to organic growers.  A ‘non-chemical’ means of controlling TPP is therefore desirable.  Mesh crop covers are such a non-chemical control: they are akin to fly screen for crops. They are extensively used in Europe for controlling a wide range of pests on an equally wide range of crops by both organic and mainstream growers. 


Prior research by the FFC made the serendipitous discovery that mesh crop covers are not only an effective barrier to TPP but they are also achieving significant potato blight (Phytophthora infestans and/or Alternaria solani) control.  A correlation has been shown between a reduction in UV a & b light levels and blight and also TPP symptoms. 

As mesh can keep out a wide range of potato insect pests, including those that are resistant to insecticides, such as tuber moth, it has the potential to be a single non-chemical solution to both insect pests and blight on potatoes.  As potatoes are the 4th most important food crop globally, with more grown in the developing world than the developed world, the potential global impact in terms of reduced agrichemical use is considerable.

However, potato aphids, mostly Myzus persicae, are penetrating the mesh, even mesh that has sufficiently small holes to exclude winged (and wingless) adults.  Once inside the mesh, their populations can explode due to the absence of beneficial insects, in effect, it is an unintentional experiment on the level of biological control of aphids. 

Mesh with sufficiently small holes to exclude immature aphid instars has been tested and resulted in a second serendipitous that the fine mesh appears to be modifying the under mesh micro-climate resulting in increased yields, while also improving blight control. 
Such very fine mesh has the potential therefore to completely control all potato insect pests, as well as blight and increase yield through entirely physical means. 

The field day will provide an opportunity to hear more about the research as well as viewing mesh on potatoes.


Benchmarking Onion Variability 2016-17

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.

Yield assessments show considerable variation, limits imposed by population, growth of individual plants, or both

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.

Management Action Zones – If population is low work for better establishment next season. If plants are small see if there is something that can be done this season


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.

Maps of canopy cover created from the CoverMap smartphone application show significant variability across the paddock. Canopy increase is seen over time in two maps created a week apart

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.

Wide variation within the area new to onions does not follow artificial rain or topographic drainage patterns. This photo is of the area shown far right in the cover maps above.

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.

This project is in collaboration with Plant and Food Research and is funded by OnionsNZ and the MPI Sustainable Farming Fund.

We also appreciate the support of growers, seed companies and our MicroFarm sponsors Ballance AgriNutrients, BASF Crop Protection and the Centre for Land and Water.


Onion Crop Development

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.

The lasting effect of a heavy (artificial) rain event pre-emergence (right panel) shows low population and poor growth compared to areas without heavy rain (left panel)
The lasting effect of a heavy (artificial) rain event pre-emergence (right panel) shows low population and poor growth compared to areas without heavy rain (left panel)

However, we also see other areas that have poor crop development that are outside the area irrigated to create the artificial rain event.

Wide variation within the area new to onions does not follow artificial rain or topographic drainage patterns.
Wide variation within the area new to onions does not follow artificial rain or topographic drainage patterns.

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.

Onion Crop Research Plan

After identifying areas within paddocks that had yields limited by different probably causes, we conceived the idea of Management Action Zones (MAZs).

Yield assessments show considerable variation, limits imposed by population, growth of individual plants, or both
Yield assessments show considerable variation, limits imposed by population, growth of individual plants, or both

Some areas showed that yield was limited by plant number: establishment was poor. Others had the expected population, but low biomass: the plants were small due to some other limiting factor.

If we can identify zones easily, and determine the causes, we should be able to target a management response accordingly. So for this season, we set out a revised research aim.

What we want to know:

  • Can we successfully determine a management action zone in a field?

Why do we need to know this?

  • Develop a tool to increase uniformity and yield outcomes
  • Develop a tool to evaluate management practices and crop productivity

If we want to successfully determine a management action zone in a field then there are two main steps to achieve in this year’s work:

  • Confirm the relationship between digital data and crop model parameters
    • Does the relationship stay constant over time and sites?
    • How early in growth can a difference be detected?
    • Can the relationship be used to show a growth map across a field?
  • Develop an approach to gather information and ways to input and display results, initially using a website approach.
    • Can we integrate a plant count and yield information to start developing a management action zone?
    • How should this be put together in a way growers can start to use to gather information about their crops?

At the MicroFarm, we established six research zones based on paddock history and excessive wetness at establishment.

We have three paddock histories: two years of onion production with autumn cover crops of Caliente mustard, two years of onion production with autumn cover crops of oats, and no previous onion crops planted after previous summer sweetcorn and autumn sown rye grass. In each of these areas, we deliberately created sub-zones  by applying about 45mm of spray irrigation as a “large rain event”.

Artificial heavy rain event applied after planting and before emergence
Artificial heavy rain event applied after planting and before emergence

The impact of the artificial rainstorm is evident on images taken at the end of November.

The lasting effect of a heavy (artificial) rain event pre-emergence (right panel) shows low population and poor growth compared to areas without heavy rain (left panel)
The lasting effect of a heavy (artificial) rain event pre-emergence (right panel) shows low population and poor growth compared to areas without heavy rain (left panel)

Vision System for Onion Crops

Effective Sensing for Robotic Tasks- Still a Challenge

Chee Kit Wong

Kit Wong
Callaghan Innovation


Effective and reliable sensing for the performance of robotic tasks, such as manipulation in the outdoor environment remains a challenging problem.

While commercially available solutions such as ASA-LIFT are available for specific tasks and crops, and for operation in specific conditions, the systems are either not cost effective and or physically unsuitable for specific farming conditions and practices.

This research proposed to develop a mobile robot system with flexibility to adapt and with intelligence to cope with natural variability; through a two-fold aim utilising vision for navigation and manipulation. This talk discussed some of the recent developments on these aspects.

In particular, the talk focused on a novel approach that analyses point cloud information from a time-of-flight (ToF) camera to identify the location of foremost spring onions along the crop bed, for the intention of robotic manipulation. The process uses a combination of 2D image processing on the amplitude data, as well as 3D spatial analysis, extracted from the camera to locate the desired object.

Whilst the experimental results demonstrated the robustness of this approach, further testing was required to determine the ability of a system to cope with different scenarios that exist in the naturally varying environment.

For validation, the vision system was integrated with a robotic manipulation system and initial results of the investigation were presented.

Mapping Onion Canopies

Investigating Technologies to Map Onion Crop Development



Dan Bloomer and Justin PishiefCentre for Land and Water


The OnionsNZ/SFF Project “Benchmarking Variability in Onion Crops” is investigating technologies to map onion crop development. The purpose is to better understand variability and to gather information to inform tactical and strategic decision making.

An AgriOptics survey provided a Soil EM map of the MicroFarm which was used as a base data layer and helped select positions for Plant & Food’s research plots.

As the crop developed, repeated canopy surveys used a GreenSeeker NDVI sensor and CoverMap, a Smartphone application. Both were mounted side by side on a tractor fitted with sub-metre accuracy GPS.  Altus UAS provided UAV survey data including MicaSense imagery with five colour bands captured. A mid-season 0.5 m pixel NDVI satellite image was captured.

Both ground based systems had difficulty recording very small plants. GreenSeeker data were dominated by soil effects until a significant canopy was present. Once plants could be seen in photographs, the CoverMap system was able to distinguish between plants and soil.

Direct photos of Plant & Food plots were processed to calculate apparent ground cover. A very strong relationship was found between these and actual plant measurements of fresh leaf weight and leaf area index – both strongly correlated to final crop size.

Attempts to directly correlate the map layers with Plant & Food field plot measurements were frustrated by inadequate or inaccurate image location. Onion crops have been found highly variable over small distances. The GreenSeeker only records a reading every four or five metres, and CoverMap about every 1.5 m. Compounded by errors of a metre or more, finding a measurement to match a 0.5 m bed plot was not possible. Similarly, the UAV and satellite images, while able to identify plots, did not initially show correlations.

Using ArcGIS, fishnets were constructed over the various canopy data layers and correlations between them found at 5 m and 10 m grids. The 10 m grid appears to collect enough data points even for the GreenSeeker to provide a reasonable if not strong correlation with other canopy layers.  Similar processes are being used to compare soil and canopy data.

After one season of capture, there appears to be merit in using an optical canopy cover assessment as plants develop. Once full canopy is achieved, the NDVI or a similar index may be better. Colour image analysis will be tested as a method of recording crop top-down as a measure of maturity and storage potential.

We were not successful in mapping yield directly, but did identify a process for creating a yield map based on earlier crop canopy data.

Onions – Plant and Crop Modelling

Understanding Variation in Onions and Potential Causes

Bruce Searle, Adrian Hunt, Isabelle Sorensen, Nathan Arnold, Yong Tan, Jian Lui   Plant and Food Research

Onion growth, development, quality and yield can vary significantly within a field. This can be observed as inter-plant variability, where two plants side by side or within very close proximity vary significantly in size and maturity or quality from each other. Additionally, spatial variability in between different areas of the field has been observed. Put these two scales of variability together and there can be significant reduction in yield and profitability for growers.

It has been estimated that a modest increase of yield from 45-50t/ha associated with a 10% reduction in size variability can increase gross margins by $1700 per hectare. Add to this the fact that variability in the field results in variability in bulb maturity and therefore storage losses, minimising variability has a strong value proposition for growers.

To minimise variability we need to know how much variability is present, what causes it and when it occurs. We used soil EM maps to identify four zones across an onion field. Within each zone we recorded variability in growth and development of individual plants to better understand plant to plant variability and how this affects overall yield variability within a field.

We also monitored crop characteristics such as leaf area across a plot and light interception to understand how yield accumulated across the different zones. Soil moisture and temperature was logged at different depths for the duration of growth.

Profit Mapping Variability in Onions

Profit Bands Across A Paddock

 Justin Pishief

Justin Pishief and Dan Bloomer
Centre for Land and Water


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.

Enhancing Value of New Zealand Onions

Onions New Zealand Research project


Dr Jane Adams
Research and Innovation Manager, Onions New Zealand Inc.

The New Zealand onion industry expects to further develop high value export markets, particularly in Asia, which could see its exports double to $200million by 2025. To realise these export opportunities the industry needs to improve efficiency and consistency of production and reliably supply high quality onions.

Currently industry average yields for brown onions vary between 33 and 50t/ha depending on season, which are significantly below demonstrated potential average yields of 100t/ha. Competition for productive land mean growers must maximise both productivity and crop value, while also meeting requirements to sustainably use resources and minimise environment impacts.

To help the industry achieve these objectives Onions New Zealand developed a project ‘Enhancing the profitability and value of NZ onions’, in collaboration with LandWISE Inc and Plant and Food Research, to understand causes of low yields and variable quality of onion crops and to develop tools to help growers monitor and manage crops. The project received additional funding from Ministry of Primary Industries Sustainable Farming Fund and commenced in July 2015.

In the first season of the project a crop of cv Rhinestone onions was grown on the LandWISE MicroFarm to allow easy access for both LandWISE and Plant and Food Research scientists to assess crop development and test methods and tools for monitoring the crop and environment at regular intervals.

Four monitoring zones were established across the trial paddock for detailed measurement of plant growth and crop development. Several tools and techniques were tested for obtaining digital data of site and crop attributes. 

An important part of the project is the involvement of local growers in discussion of progress results and use of monitoring tools and advice on crop management.  

MicroFarm Cover Crops Incorporated


Many thanks to Nicolle Contracting and True Earth Organics for getting our winter cover crops incorporated today.


This winter saw a repeat of last year’s split planting of Caliente Mustard and Oats to compare effects on soil, disease and plant growth. Seed was provided by True Earth Organics.

To gain benefit from the fumigant properties of the Caliente, it must be soil incorporated as soon as possible. This is why we have the two tractors closely following, one mulching the crop, the other incorporating the residues.

Mulching mustard - reasonable biomass, but some insect damage reducing leaf mass
Mulching mustard – reasonable biomass, but some insect damage reducing leaf mass
Mulching before incorporating oats

Onions are to be planted in this area for a third season in succession. Our onion crop will also include a new area that has never had onions planted before. As part of our collaboration with Onions New Zealand and Plant and Food Research, we will compare the performance of crops in the different areas.