Category Archives: Agronomy

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)

Onion Crops Sown

As part of our ongoing research project with Onions New Zealand, a new crop was sown on 6 September 2016.

Sowing onion seed at the MicroFarm
Sowing onion seed at the MicroFarm

Harvey from G & J Steenkamer planted the crop using Rhinestone seed donated by Vigour Seeds and treated for us by Seed and Field Services. We are very grateful for their continuing support.

We’ve aimed at a population of 580,000 plants/ha. With 8 rows in our 1.82m wide beds, we have seed at 72mm spacing in the row.

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G& J Steenkamer sowing our onion crop.

After last harvest the beds, but not wheel tracks, were ripped to 450mm depth.  Autumn planted Caliente and oat cover crops were mulched and incorporated in late June and the ground left fallow.  Prior to sowing it was hoed and rolled.

Rain after planting had only minor impact, with a little soil capping in some areas.

weatherdata

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

oatsvsmustard

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

incorporatecovercrops

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
mulchingoats
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.

On-Farm Fertiliser Applicator Calibration

Guidance for farmers – check performance of fertiliser spreading

DanBloomer200

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.

Farmers getting value from soil EM maps

Chris SmithChris Smith
AgriOptics NZ Ltd

An electromagnetic (EM) soil conductivity Survey maps the variability in soils characteristics; these values are strongly influenced by many factors but mainly soil texture, soil moisture at the time of the survey as well as bulk density and salinity.

Combining this data with topography data collected at the time of the survey gives the farmer a powerful management tool for creating management zones for various aspects of his business, including amongst other things; managing water, zonal soil sampling, improving yield and pasture performance where soil characteristics are the limiting factors, managing inputs to targeted placement, highlighting and reducing the environmental impacts or risks.

AgriOptics has been conducting EM surveys since 2011, with various clients and in many differing scenarios and enterprises, covering over 20,000ha in that time.

This presentation explained what an EM survey is and what information the farmer receives from the service and how the different layers of data from that survey are being utilised by farmers in the South Island with both its direct and indirect uses, and how that translates into a dollar value to those clients.  Examples of both dairy and arable farmers each with not only common goals but their own specific issues and requirements were given.

Precision in Queensland Vegetables

Lessons in frustration, improvisation and unexpected outcomes

Ian LaydenIan Layden
Department of Agriculture and Fisheries, Queensland

It’s widely promoted that precision agriculture (PA) has the potential to offer producers a myriad of exciting opportunities for improving crop performance and ideally profits. However, the reality seems to suggest that in order to unlock any significant benefits a lot of work and importantly knowledge generation will be required.

Arguably, progressing PA in vegetable systems will require producers, consultants and R&D providers to accept technology and systems that aren’t fit-for-purpose and the numerous obstacles that exist in terms of equipment compatibility, data processing and management, service and support and whether the return-on-investment (ROI) outweighs the costs.

Despite the numerous reasons not to invest and adopt PA practices, vegetable producers and agronomists have achieved a number of essential adoption milestones, though typically this hasn’t been easy or straightforward.

Recent work in Queensland suggests that the adoption of advanced PA technologies and practices (e.g. crop sensing, yield monitoring, soil mapping and variable rate applications) is occurring, though often the process and outcomes are either unintended or unexpected. This work also indicates that diverse relationships and delivery methodologies may be required if industry wide adoption of PA is to occur.

This presentation used examples from the process of optimisation and validation of PA in vegetable systems in Queensland including producer and consultant survey data. The presentation also used examples from outside agriculture to illustrate that through experiencing difficulties and failures actually may improve the adoption process. This has implications for producers, consultants, investors, program managers and policy developers.

Investigating variability in potato crops

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.

DSC_4288sm
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”.