Category Archives: LandWISE People

LandWISE People, Precision Agriculture, Research

The Value of Smart Farming

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header1525 and 26 May 2016

Havelock North Function Centre

LandWISE 2016: The Value of Smart Farming is our 14th Annual Conference! Details are on-line and registrations open.

This year we are proud to host a delegation from Australia with two dozen growers and advisors crossing the Tasman to join us. The conference is the central point of their week long tour from Auckland through the Bay of Plenty to Hawke’s Bay and the Manawatu.

Tour leader, Ian Layden is also one of our keynote speakers. Ian and the Queensland growers have been trialing precision vegetable growing, assessing the same sort of things that are of interest to LandWISE members. We look forward to learning from their experiences.

A carrot harvester fitted with a yield monitor as part of Queensland Precision Vegetables research. (C) Image from SPAA 2015 Conference Proceedings
A carrot harvester fitted with a yield monitor as part of Queensland Precision Vegetables research. (C) Image from SPAA 2015 Conference Proceedings

On a different angle, Charles Merfield will present results from a review of the agricultural uses of plant biostimulants. Plant biostimulants are diverse substances and microorganisms used to enhance plant growth. The global market for biostimulants is projected to increase 12 % per year and reach over $2 billion by 2018!  Despite the growing use of biostimulants in agriculture, many in the scientific community consider biostimulants to be lacking peer-reviewed scientific evaluation. Merf’s review will get us up to date with the science that is available on this topic of great and growing interest.

In 2015, LandWISE and Plant and Food Research began a three year project investigating variability in onions crops. We will report results from our first year’s activities, ranging from individual onion measurements to images from satellites and a swag of things in between.

Mapping canopy development with smartphone app - camera shrouded to avoid shadow effects
Mapping canopy development with smartphone app – camera shrouded to avoid shadow effects

We hear more and more about “big data” and “data analytics” and “value chains”.  These need not be scary! and we have several experienced speakers to let you in on the secrets. Join Alistair Mowat, James Beech and Megan Cushnahan as they explain value chains, the value of massive amounts of image data to identify signatures of specific crop factors, and how big data gives insights that were previously unseeable.

The party wouldn’t be complete without a peek at field robots and UAVs. Do you know the Civil Aviation rules about flying a drone – even on your own place?

Check out the programme, register and we’ll see you in Havelock North!

Thanks to Our Loyal Platinum Sponsors!
Thanks to Our Loyal Platinum Sponsors!
AgTech, Automation, LandWISE People, Precision Agriculture, Research

A visit to Climate Corporation

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LandWISE’s Dan Bloomer joined a large group of New Zealand ag-tech organisations for a week getting to know the Silicon Valley venture capital and tech start-up scene.

The first visit was to Climate Corporation, recently acquired by Monsanto. How can you not be impressed by a three metre 3D projection screen? One you can control from a tablet, make it spin like the earth, view the globe from any angle, and project any theme you like.

ClimateCorpGlobe
Climate Corp Chief Technology Office, Mark Young, demonstrates the globe.

Climate Corporation seeks to understand the world and its climate, map it in real time and make useful information available to the world’s farmers. We saw time lapse of global cloud cover, near real time views of sea surface temperature and as shown above, global ground cover allocated to food for animals and food for people.

But their interest is wider than the weather. The Climate Corporation aims to build a digitized world where every farmer is able to optimize and flawlessly execute every decision on the farm.

They are investing heavily in agronomy and creating growth models to help predict crop development.   To achieve greater seed placement accuracy they developed SpeedTube, a precision elevator to replace drop tubes. This is said to allow more precise seed spacing at twice the normal planting speed. Same quality at twice the rate? Sounds attractive!

This visit was organised by Wharf42, NZTE, Callaghan Innovation and the Silicon Valley Forum.

LandWISE People, Research

One Minute Questionnaire #2

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Responses to the first one minute question covered a wide range of things for which people sought relief!

While self washing and folding clothing is of interest to all, it’s not perhaps one we are well placed to resolve, at least not rapidly. Other requests included getting wide machinery to fit through standard gates, reducing time spent on repetitive tasks such as spraying and weed control, and simplifying compliance.

A number of people identified recording information as their key bugbear. So that’s one we follow up with Question Two.

Data

Question of the Week:

What sort of information is recorded manually, then collected and entered into a computer so it can be used?

Email your reply -click here

Or use the form below

Fields marked with an * are required

Thanks for sharing your problems; believe it or not, we really want to know!

LandWISE People, MicroFarm, Precision Agriculture, Projects, Research, Sensing

BioRich Tractor for MicroFarm

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The arrival of a BioRich sponsored tractor at the LandWISE MicroFarm will support precision farming research efforts.

BioRich_JD900HCWeb
The John Deere high clearance cropping tractor is set to match the onion beds at the MicroFarm.

The John Deere cropping tractor has been set to a 183cm wheel track to fit the onion beds planted in early August. It’s first role is to act as a carrier of sensors that are used to map crop development.

We are delighted with the tractor. After much investigation into options for a sensor carrier, we finally landed on a high clearance cropping tractor as the ideal machine. Then, after searching wide and long, we discovered there was one sitting on our back door.

BioRich Principal, Mike Glazebrook is a LandWISE founding member and past Chairman. He said he was keen to support  the work being done at the MicroFarm as he sees it as of benefit to the community. There is obvious alignment with LandWISE objectives for sustainable production.

BioRich Limited is an organic waste recycling company. It’s main activity is capturing organic material that would otherwise be wasted, or cause pollution, and turning it into rich compost. Where it is practical to do so it also seeks to extract stock food and energy from organic material that would otherwise be wasted.

Every year, throughout New Zealand, many thousands of tonnes of organic “waste” is dumped into landfills or is inappropriately discharged to land. Once dumped much of this material breaks down in an uncontrolled manner and releases greenhouse gases into the atmosphere and pollutants into our waterways.

Meanwhile most of New Zealand’s cultivated soils have been steadily deteriorating. This is due to both to a decline in soil organic matter and a depletion of minerals and nutrients.

Hence BioRich’s mission is to divert organic matter (carbon) from ending up in places where it can do a lot of harm – in our atmosphere and water – and putting it somewhere it can do a lot of good – in our soils.

Automation, LandWISE 2015, LandWISE People, Precision Agriculture, Sensing, Soil, Tillage, Uncategorized

The Mechanical Farm of 2030

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LandWISE 2015 Presenter, Dan Bloomer

 

DanBloomer200
Dan is the Manager of LandWISE Inc, an independent consultant, and a member of the Precision Agriculture Association of New Zealand Executive.

In 1981, John Matthews of the UK National Institute for Agricultural Engineering described what a farm would look like in 2030; a fifty year horizon.

“The mechanical farm of 2030” identified four factors that would influence the farm of 2030; social factors including employment, preservation of the environment, animal welfare and primary energy sources.

NIAE_Cover

Soil quality and alternative machinery were high on their list. Computers and robotics were available but GPS, internet and wireless were not.

In 2015, with all the benefits of knowing what happened in the last 35 years, we revisited the question to ask, “What will a cropping farm look like in 2030?” Were John Matthew’s predictions of technology on-track? And importantly, what must farmers do to ready themselves for next year, five years and fifteen years down the track?

The general consensus was a resounding round of applause for John Matthews. The issues he identified continue to be key drivers today. The technological developments he envisioned are progressing towards the 2030 deadline with examples of commercially developed gantries now being tested on farms in Europe.

MatthewsGantry
The NIAE Gantry image from John Matthews paper
The ASALift Gantry tractor in 2013
The ASALift Gantry tractor in 2013

John Matthews article included a robotic harvester. We know the computing and actuation required for that is still tricky, but it seems quite probable robotic harvesting will be feasible and possible it will be relatively common by 2030.

The NIAE robotic harvester image from Matthews' paper
The NIAE robotic harvester image from Matthews’ paper

Perhaps his control tower windows are more likely to be computer monitors, and he didn’t know about smart phones, but his vision of the role computing would play is remarkably close – though perhaps thanks to Moore’s law and compounding development we have already got further than he estimated.

MatthewsComputer
The NIAE image of a farm computer appears to have a rack for storage disks, but also shows a microphone and aerial perhaps for wireless communications.

Maybe the design (how) is different to now, but much of the what of John Matthews’ predictions suggests he deserves a high score.

 

Agronomy, Automation, LandWISE 2015, LandWISE People, Precision Agriculture, Sensing

Agri-Intelligent Systems: robots, data, and decisions

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LandWISE 2015 Presenter, Tristan Perez

Tristan Perez Professor of Robotics and Autonomous Systems, Queensland University of Technology, QLD, Australia
Tristan Perez
Professor of Robotics and Autonomous Systems,
Queensland University of Technology, QLD, Australia

Since the 1960s, agriculture has seen significant advances in agrochemicals, crop and animal genetics, agricultural mechanisation and improved management practices. These technologies have been at the core of increased productivity and will continue to provide future incremental improvements. Data analytics, robotics, and autonomous systems are transforming industries such as mining, manufacturing, and health. We are starting to see automation of single agricultural processes such as animal and crop remote monitoring, robotic weed management, irrigation, nutrient decision support, etc. However, we envisage that the integration of these technologies together with a systems view of the farming enterprise and its place within the agri-food value chain will trigger the next wave of productive innovation in agriculture.

The challenge of the next agricultural revolution is to assist farming enterprises to make the management and business decisions that will optimise inputs such as labour, energy, water and agrochemicals and explicitly account for variability and uncertainty across the production system and along the agri-food value chain.

The opportunity for increased profitability, sustainability and competitiveness from finer-scale sensing and whole-farm decision-making and intervention requires farmers to have greater access to digital data and technologies to extract information from data. The agricultural landscape will rapidly change due to low-cost and portable ICT infrastructure.

Agri-intelligence is the integrated collection of tools and techniques – from robots, unmanned airborne vehicles (UAVs) and sensor networks to sophisticated mathematical models and algorithms – that can help farmers make sense of large amounts of data (agronomic, environmental and economic) to make risk-informed decisions and run their farms more profitably and sustainably.

Perez AgriIntelligence

 

The figure below shows the ubiquitous emerging vision of a farm in the second machine age, where computer systems are used to augment human perception and capacity for decision making in complex situations.

PerezAgriIntelligentFarmSystem

The farming enterprise is considered a system that interacts with the environment (through climate, markets, value chain, etc.) The key objective is to make sound decisions about management in order to optimise inputs, yield, quality, and at the same time make the system robust against yield and quality volatility due to climate, commodity market fluctuations, and incomplete information about the state of crop, soil, weeds and pests.

Agronomy, Controlled Traffic Systems, LandWISE 2015, LandWISE People, Nutrients, Precision Agriculture, Projects

Testing On-Farm Fertiliser Spreading

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LandWISE 2015 Presenter, Dan Bloomer

DanBloomer200
Dan is the Manager of LandWISE Inc, an independent consultant, and a member of the Precision Agriculture Association of New Zealand Executive.

The SFF “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.  They wanted to know that spreading was acceptable.

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.

There are many protocols internationally relating to the spreading of fertiliser products. Lawrence (2007) compared six test methods.

1400 tray matrix used to collect 18 simultaneous transverse tests on a Transpread “W” twin chain spreader From Lawrence, 2006
1400 tray matrix used to collect 18 simultaneous transverse tests on a Transpread “W” twin chain spreader, From Lawrence, 2006

Most used 0.5 m trays organised in a single transverse row to capture the spread pattern of the spreader. No account is taken of the longitudinal variation between individual rows when multiple tests are carried out.

Larwnce Comparisons

The results of the test are given as the bout width where the coefficient of variation (CV) does not exceed a specified level. In all cases the maximum allowable CV is 15% for nitrogenous fertilisers and 25% for low analysis fertilisers.

An On-Farm Protocol

There is no set method dor assessing uniformity. On-farm testing could use a set number of collectors per swath (spacing changes with swath width) or a set spacing between containers (container number varies with swath width). Farmers can decide.

There are however some important principles:

  • Uniformity requires collection of samples from a spreading event and calculation of a uniformity value.
  • Set equipment up correctly according to manufacturer’s instructions
  • Ensure the spreader is horizontal, and at the correct height off the ground
  • Use standard test trays, given the need for baffling to stop fertiliser bouncing out
  • Ensure the spreader is driven well past the trays to capture all fertiliser
  • If a larger sample is wanted, two or more runs at the chosen application rate should be made rather than applying a higher rate.
FertSetUp
Set equipment up correctly according to manufacturer’s instructions
FertTrays
A line of trays laid out across the full width of spread to catch fertiliser. Use standard test trays, given the need for baffling to stop fertiliser bouncing out

Weighing samples is complicated by the very small quantities involved – often a single prill in the outer containers. Scales weighing to 0.01g are required, but satisfactory options are readily available at reasonable price.

An alternative is to assess the volume of fertiliser captured in each tray. Disposable syringe bodies make good measuring cylinders.

FertTestRack
Disposable syringe bodies make good measuring cylinders

Determining a field uniformity will involve either physical or theoretical over-lapping of adjacent swaths.

FertOverlap
Determining a field uniformity will involve either physical or theoretical over-lapping of adjacent swaths

On-line software is being developed to process data and generate statistical reports. Key outputs will be measured application rate, the CV at the specified bout width and the bout width range at which CV is within accepted limits.

Test spread-pattern checks performed to date show there is a need for wider testing by farmers. Unacceptable CVs and incorrect application rates are not unusual.

 

There remains some question about the percentage of fertiliser caught in some types of tray

The SFF project is co-funded by the Foundation for Arable Research and the Fertiliser Association

FertResearch

FAR

 

Agronomy, Automation, LandWISE 2015, LandWISE People, Planting, Precision Agriculture

Micromanaging Your Planter

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LandWISE 2015 Presenter, John Ahearn

John Ahearn
John Ahearn, GPS Control Systems

Modern planters are amazing machines.  Electric and hydraulic drives with precise speed control have replaced ground engaging wheels.

Variable rate is achieved using prescription maps.  Individual row control is achieved using GPS location. So seed populations, fertiliser rates and spray rates are accurate. Up hill, down hill, on curves, headlands, and point rows – the precise rates are applied with zero over-lap.

How do you check your new planter is performing as advertised?  Before looking for improvements we need to get a benchmark of where the performance is now. During planting there are some problems you can identify and fix on the go and some you can’t.  The trick is to record planter data, match it with yield data, and use that analysis to fix or modify the planter for next year.

Most planters have a simple population monitor but the addition of a few extra sensors, and a data management tool, gives an idea of how these variables affect your planter, and ultimately – your yield.  Variables to monitor include singulation, ground contact pressure, ground wheel slippage speed.

When you know what happened during planting, and can compare that to the end of season yield map, you can then figure out the impact these variables have on yield.  That yield impact can be converted to a dollar cost which leads to a discussion on planter improvements for an old planter, and a set-up review for a new planter.

An aftermarket planter control system can be retrofitted to most old planters.  The system will monitor and record the planter variables during the planting operation for each field.

Ideally, the same system will also be used to collect and record yield data during harvest. This completes the loop for data collection and gets all the data in the same place on the same software.  Later analysis is then simple and it is easy to identify opportunities, problems and create prescription maps for next season.

What goes on the planter?

  • Monitor /computer screen in the cab.
  • population sensors – use existing or fit newer better units
  • down force sensors and hydraulic down force control units
  • electric or hydraulic drive system for seed, fertiliser, and other inputs
  • section control

Process improvement is a continual process that leads to higher yields, reduced inputs, and higher profitability.  There are a lot of variables we can’t control, but closing the loop on planter variability finally adds some true value to your yield maps.

Agronomy, LandWISE 2015, LandWISE People, Nutrients, Precision Agriculture, Sensing, Soil, Water

“V” is for variability – origins of variation within a crop and contributions to yield

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LandWISE 2015 Presenter Bruce Searle

BruceSearle200
Bruce Searle, Crop Physiologist and Modeller Plant and Food Research

Why is a crop so variable and what can be done about it? And of that variability, how much can management practices actually affect?

Those who work with crops are well aware of this variability, but ways of addressing it are less clear.

Variability affects profitability so managing it is important. The more variable a crop is, the more the yield will be reduced compared to an even crop that produces the maximum attainable yield, and so profitability is reduced.

Increasingly, the value of a vegetable crop is dependent on providing product that meets some quality criteria – initially, usually size. This means that growing to maximise yield does not always increase profitability if the crop does not meet the quality standards. If variability is high then less of the crop will fall in the grades desired by the market and so profitability decreases.

Causes of variability are complex. It starts with the variability in seed size. Overlaid on this is variability in emergence time, variability in seedling size, variability in plant spacing and effects on competition, individual variability in relative growth rate, and differences in spatial supply of nutrients and water in the field and patchiness in pest and disease.

All these factors interact in different ways. Models are an ideal way to quantify some of this complexity and provide good insight, but the large number of measurements needed and the large number of interactions to compute can limit the value of such approaches.

Here we start to develop a conceptual framework that allows the causes of variability in a field to be identified and enables the contribution of each cause to be considered. We have called this framework the ‘V of variability’.

VforVariability

 

We group causes of variability into spread of emergence, establishment, population and growth, and examine how these factors change for crops of onions and potatoes and affect the variability of size in onions and potatoes and dry matter % of potatoes. We use this to identify how much variability is manageable and the appropriate key management practices to consider.

This framework, when linked with digital information capture at a field scale, could provide a powerful tool for management of variability.

Presentation Authors: Bruce Searle, Jeff Reid and Paul Johnstone – Plant and Food Research

 

Agronomy, Automation, LandWISE 2015, LandWISE People, Nutrients, Precision Agriculture, Sensing, Soil

Hyperspectral remote sensing to assess pasture quality

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LandWISE 2015 Presenter Ian Yule

Ian Yule, Centre for Precision Agriculture, Massey University
Ian Yule, Centre for Precision Agriculture, Massey University

A presentation by Ian Yule, Reddy Pullanagari, Gabor Kereszturi, Matt Irwin, Ina Draganova, Pip McVeagh, Tommy Cushnahan, Eduardo Sandoval.

Remote sensing methods are becoming much more accessible for end users in terms of access to results and the method in which they are presented. They can be developed into systems for herbage analysis which will measure every square meter of a farm or river catchment and publish the results in the form of a map, as below, rather than complex hyperspectral analytical measurements.  

The team at Massey University have been using a hyperspectral imaging tool called Fenix. It is flown in an aircraft usually at around 500-800 m above ground level and has been used to measure hill country properties within New Zealand in the first instance. The map below shows the level of ME in pasture for an example scene, but the major nutrients can also be mapped in this way.

MasseyHyperspectral

 

The catalyst for purchasing the sensor was the Ravensdown/ MPI, funded PGP project; Pioneering to Precision: Fertiliser Application to Hill Country.

The first scientific objective of the project is to map the nutrient concentration of pasture over hill country properties. The business objective is to provide much better information around the productivity of hill country in order to calculate the fertiliser requirements more accurately and improve the overall utilisation of nutrients.

Previous work indicated that significant financial benefit could be achieved from this approach. This is the first time that an imaging tool has been used and this is important because it overcomes many of the difficulties of on-the- ground sampling and then using these samples to represent the whole farm. It is basically impossible to capture the true variability of these properties from ground sampling.

The sensor detects light in the Visible (VIS), Near Infrared (NIR) and short wave infrared (SWIR) parts of the electromagnetic spectrum. This gives it the ability to determine the bio-chemical properties of vegetation that it observes. It has been shown to be a very robust technology for laboratory analysis and this new development takes it out of the laboratory and in the field.

The images captured in strips are mosaiced together in order to develop a single image for the whole area. Each pixel has 448 different layers of information, corresponding to 448 different wavebands which make up the spectral signature for each pixel. It is by comparing the spectral signature using a number of statistical techniques that the nutrient concentration within the vegetation can be identified.

The big advantages with this approach are that the results can be presented in the form of a map, information can be produced quickly with limited need for laboratory based chemical analysis. All of the complex statistical and analysis processes have happened in the background and the results can be presented with in a Geographical Information System (GIS). In a GIS environment data can be linked to decision making software which will help farmers decide on the optimal fertiliser policy for the farm.

Acknowledgement. The image was produced from a survey from the PGP Project, Pioneering to Precision: Fertiliser application in hill country, which is funded by Ravensdown Fertiliser Cooperative and the Ministry for Primary Industries MPI.

New Zealand Centre for Precision Agriculture, Massey University, Institute of Agriculture and Environment. Palmerston North, New Zealand.