Category Archives: Plant Health

AgTech, MicroFarm, Plant Health, Precision Agriculture, Projects, Sensing

Mapping Onion Canopies

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Investigating Technologies to Map Onion Crop Development

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Dan Bloomer and Justin Pishief – Centre 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.

Agronomy, MicroFarm, Plant Health, Research, Sensing, Soil

Onions – Plant and Crop Modelling

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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.

Agronomy, AgTech, Drainage, MicroFarm, Nutrients, Plant Health, Planting, Precision Agriculture, Projects, Research, Sensing, Soil, Water

Profit Mapping Variability in Onions

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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.

Agronomy, AgTech, MicroFarm, Plant Health, Planting, Precision Agriculture, Projects, Research, Sensing

Enhancing Value of New Zealand Onions

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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.  

Agronomy, LandWISE People, MicroFarm, Plant Health, Projects, Research, Soil, Tillage

MicroFarm Cover Crops Incorporated

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

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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
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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.

Plant Health, Projects, Research

Understanding Biology and Biofertilisers

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Smart Biology – Trial on your own Farm

MerfieldCharles Merfield

Future Farming Centre

There has been a phenomenal growth of biostimulants and biological fertilisers. A wide range of claims are made for these products and it can be hard for farmers and growers to tell fact from fiction.  Unfortunately, there is no rule of thumb or other simple way to separate good from bogus products: the only way is to do experimental trials.

The good news is that you don’t need lots of expensive research equipment for such experiments – the best place to do them is on your crops, your pasture and your stock.  This is because in many cases, the effects of the products are highly climate, soil, plant and animal specific – what works on one farm may not work on others.

The answers you get will also have the highest level of applicability to your business.  This is also why you should be wary of experimental results that weren’t done on a production system similar to your own property – they may be meaningless to your property.

Conducting your own experiments is also not that difficult.  There are some key things to get right including: treatments; a null control; the right duration; make sure it’s randomised and replicated, and measure what matters.

While there is a bit to learn to get such experiments right, it is not rocket science, and when you do your own experiments you can also discover a lot more about your farming and growing systems which can help you run your business better plus it puts more power in your hands.

For more information, see the on-line FFC Bulletin article or download as a pdf.

Agronomy, AgTech, Nutrients, Plant Health, Precision Agriculture, Projects, Research

On-Farm Fertiliser Applicator Calibration

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Guidance for farmers – check performance of fertiliser spreading

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Dan Bloomer
LandWISE

Fertiliser application calibration procedures suitable for farmers applying nutrients with their own equipment have been developed.  Guidelines and a web-based calculator (see www.fertspread.nz) support on-farm checks to ensure and demonstrate application equipment is performing to expectations.

Farmers and agronomists had noticed striping in crops, especially when spreading bout widths increased to match wide sprayer bouts. Visible striping is indicative of very significant non-uniform distribution and yield loss.

A calibration check includes assessment and correcting of both application rate (kg/ha) and uniformity (CV). Farmers indicate determining the rate is reasonably easy and commonly done. Very few report completing any form of uniformity assessment.

FertSpread calculates uniformity from data from a single pass and mathematically applies overlap using both to and fro and round and round driving patterns. Test spread-pattern checks performed to date show there is a need for wider testing by farmers. Unacceptable CVs and incorrect application rates are the norm.

Fertiliser applicator manufacturers provide guidelines to calibrate equipment and some newer machines automatically adjust to correct distribution pattern based on product properties and comparing a test catch with “factory” test data.

The efficiency of catch trays is called into question. While we believe the collection tray data is acceptable to assess evenness of application, the application rate should be determined by direct measurement of weight applied to determined area.  Weighing samples involves very small quantities so scales weighing to 0.01g are required. Satisfactory options are readily available at reasonable price.

An alternative approach uses small measuring cylinders or syringe bodies to compare applied volumes. While not able to assess alternative driving patterns, this can give a direct and very visual immediate view of performance.

The Sustainable Farming Fund “On-Farm Fertiliser Applicator Calibration” project arose from repeated requests by farmers for a quick and simple way to check performance of fertiliser spreading by themselves or contractors. It was co-funded by the Foundation for Arable Research and the Fertiliser Association.

Plant Health, Precision Agriculture

Increasing on-farm productivity and sustainability through Precision Agriculture

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John McPhee
Tasmanian Institute of Agriculture, Burnie

A project involving an industry representative group, the private sector, Tasmanian Institute of Agriculture and collaborating growers aims to facilitate uptake of PA technologies, with a focus on the vegetable industry.  Through the use of six commercial farm case study sites spread across a range of soil types and cropping enterprises, the project aims to:

  • raise awareness and increase knowledge of PA to aid adoption
  • identify and raise awareness of on-farm variability of soils and crops
  • provide advice regarding variable crop management and application of inputs
  • share experiences and develop networks

Project activities in the first year have focused on:

  • accumulating pre-existing data layers (primarily EM38 and elevation derived layers) and mapping case study sites for soil pH
  • collecting NDVI imagery for use as a scouting tool to aid crop management
  • sample harvests to determine the variability of crop yield and quality in a range of crops
  • planning and holding a PA Expo, allowing service providers and technology dealers to promote their products to the agricultural community

A major limitation at this stage is the relative lack of access to yield monitoring equipment for most vegetable harvesters.

In the first season of field work, harvest samples have been collected from accurately surveyed points in crops at densities ranging from 1 – 5 per ha.  Data from these harvest samples show that the variation in crop yields ranged from 2.2 fold in the best case (poppies) to nine fold in the worst case (processing potatoes).  Data will be analysed to determine correlations between crop yield and quality and underlying characteristics derived from map layers (e.g. EM38).

All sample points are located with RTK accuracy to allow inter-season sampling from the same locations (either manually or with yield monitors as they become more available) to determine if yield responses are consistent between seasons and crops.

Plant Health, Planting

Investigating variability in potatoes

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Sarah SintonSarah Sinton
Plant and Food Research

Final potato crop yield is a sum of its parts; each individual plant contributes to it. Should some of these individuals perform below potential, overall yield will be reduced accordingly. Yield variation within a crop is caused by biotic and abiotic factors, which could range from the wholesale effect of soil compaction restricting root growth across the entire field or be an outbreak of patches of disease causing the early death of individual stems or plants.

Nationally, potato yields average 55- 60 t/ha, which are not economically sustaining for many growers, and well below the 80-90 t/ha potential yields predicted by crop models. This was confirmed in a Canterbury survey of 11 process crops in the 2012-13 season where the crops had different histories, management and cultivars. All crops had a similar overall rate of yield reduction, largely caused by soil borne disease and soil physical constraints.

The survey showed that individual groups of healthy plants in a crop did achieve up to 90 t/ha (Fig. 1).   However some groups of plants yielded as little as 30 t/ha, due to Spongospora and Rhizoctonia infection, soil compaction and/or inferior seed quality.

Figure 1 Final yields from groups of individual plants that were: both healthy and growing in compaction-free soils (yellow bar); had soil compaction together with Spongospora (root galls); had severe Rhizoctonia stem canker; had both diseases and were growing in compacted soils
Figure 1 Final yields from groups of individual plants that were: both healthy and growing in compaction-free soils (yellow bar); had soil compaction together with Spongospora (root galls); had severe Rhizoctonia stem canker; had both diseases and were growing in compacted soils

Last year, an intensive study of three Canterbury crops showed that some areas of the crops reached potential and that others were limited by soil borne disease infection and water supply.

A field experiment at Lincoln is currently investigating how bed shape, subsoiling and irrigation regime are affecting crop production, and future work will look at how improvements to seed tuber production could reduce yield variability.

Agronomy, Controlled Traffic Systems, Drainage, Irrigation, Plant Health, Research, Sensing, Soil, Water

Investigating variability in potato crops

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Sarah SintonLandWISE 2016 Conference presenter Sarah Sinton is a well experienced member of a Plant and Food Research group studying potatoes.

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.

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Tuber health, disease management, soil compaction and irrigation all have ability to reduce yields

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.

Sarah is presenting some of her group’s work at LandWISE 2016. Look for “Investigating variability in potatoes”.