Calling all followers and friends of LandWISE, we invite you to become a financial member this year.
Your support is vital for LandWISE to continue doing what we do. We rely on farmer support to ensure the backing of new projects, discover new areas for research or technology adoption, and to fund field days, workshops and the development of practical resources.
LandWISE Membership is a great way to support the mission of sustainable production in New Zealand, and as a member you’ll benefit from:
Results from on-farm trials
Projects focussed on real farmer and grower problems
Regional field days and workshops on a range of topics from conserving soil to nutrient management and novel fertiliser technology
A discounted registration at the 2021 LandWISE Conference
Subscription to our annual LandWISE News publication
Membership is open to all who are interested in primary production and share our values. We hope you’ll consider becoming a member, or forward this on to a non-member if you already are!
I would like to firstly thank Dan and Debbie and the entire LandWISE team for the opportunity to be here. It seems we are all getting increasingly busy, even with these Smart Phones. However, for me this annual LandWISE conference is a must do on the calendar, and I don’t say this lightly.
LandWISE and its members are focused on a topic that is dear to me; “sustainability”. Not just sustainability, a word to be thrown around because it sounds good, but sustainability in the true sense of the word. They are active in the pursuit of sustainable farming and have built a solid track record of success.
So BASF Crop Protection is very proud to again be a key sponsor of the Annual LandWISE conference. The philosophy of LandWISE fits very well with BASF Crop Protection: providing real solutions in our primary industry that will enable us to sustainably produce more saleable yield per hectare. Without the ability to sustainably produce more, we will struggle to feed a growing population.
Many in NZ feel we are insulated, but we are all part of the global stage. A few statistics resonate daily for me. I like this one, which helps engage non primary industry people:
We need to produce more food in the next 50 years, than has been produced in the last 10,000 years.
Give or take 10% either-way and this is a hell of task with available land for production reducing constantly.
BASF celebrates its 150th birthday this year; a HUGE milestone, and one that as a company we are incredibly proud of. May the next 150 years plus be as ground breaking. But I have some worries.
I am concerned in particular, that in the next 5 years or so our industry is going to see some very experienced individuals retire. Primary production in general is going to lose these individuals, people with extensive knowledge and wisdom.
Yes, there are more being trained, however we will be losing a block of knowledge spanning four or more decades. That “old knowledge” is extremely important as to go forward we really need to be a ware of the past. Imagine having no one with experience pre Glyphosate? And that is but one of many examples.
With a slow-down in new molecule introductions, chemical resistance management is becoming even more vital. There are challenges a plenty, of which makes the journey all the more interesting.
A constant challenge of looking after the molecules we have in preventing resistance management is at a mode of action level, not an active ingredient level. We need that thinking to permeate the entire industry, developers, suppliers, users and all. If we pay attention to detail, take care to understand how resistance develops and make sure our strategies do their best to prevent it, the chemistry we have can continue to help our production systems for longer.
And to all delegates and contributors of this conference, thank you for your support and your support to LandWISE, it is HUGELY appreciated.
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.
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.
The NIAE Gantry image from John Matthews paperThe 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
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.
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.
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.
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.
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.
Delegates at LandWISE 2015 formed small groups to specify a “robot” that would address some issue of relevance.
Development of any such machine should follow a systems engineering approach. Four critical features of systems engineering described by Marcel Bergerman are:
Take a top down approach
Focus on the “what” not the “how”
Formally define requirements
A thorough test plan can be developed before the product/system is
Have a life cycle orientation
Consider all aspects from the cradle to the grave
Inter-disciplinary in nature
Systems are too complex to do it all on your own
For this exercise, delegates were asked to concentrate on the first two; define what their dream machine had to achieve and describe the constraints under which it must operate.
It is important to take this approach to avoid limiting the possible solutions too early. Deciding your scout robot would be electrically powered three wheel drive eliminates aerial, two wheel or four wheel and wind or fuel powered machines. Sure, the answer may seem obvious, but the final form of many creative solutions that are optimal are not envisaged at first.
Some examples from the exercise include:
A root vegetable harvester that must have minimise soil compaction, remove soil from the produce, minimise mechanical crop damage, and work in a range of weather conditions
A bird scarer for seed crops that must keep birds off the crop, not damage the crop, and must stay within the boundaries of the crop or farm
A pea crop sampler that picks plants randomly through the crop, analyses samples on the go in the paddock and sends the resulting information back to the office. It must cope with the height of the crop and not damage it, and work in a wide range of weather conditions and cope with pugging and mud
An automated machine to remove weeds from vegetable crops grown on beds. It may also monitor plant population and variability, health and vigour, and pests and forecast yields. Working in a range of weather and soil conditions it must have high productivity (ha/hr)
Determine insect populations across field prior to planting of spring crops, including density, location and spread of identified species – include a lure for underground species
Harvest ripe mandarins after determining brix and colour levels and sort according to quality and size with rejects dumped and in-grade fruit transferred to bins for transport
An automatic pine tree pruner that removes every branch up to a height of 6m. Cuts must be clean with no damage to branch or trunk, and it must operate in difficult terrain
An apple harvester that harvests only export grade fruit such that no post-harvest grading is required. Must work 24/7 during harvest within current tree architectures
A fresh broccoli with automated cutting, picking and packing into bins. Will select/sort according to size and colour with no flowering heads.
Miles Grafton NZ Centre for Precision Agriculture, Massey University Institute of Agriculture and Environment
crops and pastures since upgrading to spreaders capable of spreading at increased, bout or swath widths. This issue is more prevalent where fertilizer blends of products with dissimilar ballistic properties are sown simultaneously.
The problem is more obvious when applicators have purchased modern top of the range twin disc spreaders with the ability to spread at an acceptable spread pattern at tram lines at or greater than 30 m. These spreaders have increased tram or bout widths of spread from 20 – 24 m, to greater than 30 m thus reducing the number of tram lines, increasing output and reducing trafficking of the crop or pasture.
Spreading at a tram line of 30 m requires a total spread pattern to be around 45 m, allowing for a pattern overlap of around 50%, to achieve the desired accuracy (Chok et al., 2014). Given that the spreading discs are around 0.5 – 1.5 m above ground level, then fertilizer particles must be discharged at some considerable speed.
Table 1: Distances typical fertiliser particles will travel when ejected at various speeds, in a horizontal plane from 1.5 metre height. (SSP is single superphosphate at 3 different sizes, KCl is potash, MAP is mono-ammonium phosphate, DAP di-ammonium phosphate). The distances are lateral from each disk; total spreading distance is twice that in Table 1.
In order to achieve an optimal even distribution, spreaders deliver 50% of the required amount of fertilizer on each side, which is overlapped with another 50% when the vehicle makes the next run in the opposite direction, or an adjacent run in a round and round spreading pattern. Therefore, the area closest to the border of the distribution area only receives half of the recommended rate as there is no pattern overlap and the desired fertilizer response, would then be reduced as if 50% of the desired rate on average has been applied.
Border spreading refers to the capacity to reduce the application distance on the side towards the border in order to minimize the amount of fertilizer applied outside the zone. Yield spreading compensates for the need to overlap by doubling the amount of fertilizer applied in the boundary side.
Product separation can be avoided by soil testing early, then addressing fertility issues by direct drilling or broadcast application prior to the crop establishing. Then use tram line application on crops for the one product intended to be side dressed during the crop life cycle.
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
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.
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.
Set equipment up correctly according to manufacturer’s instructionsA 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.
Disposable syringe bodies make good measuring cylinders
Determining a field uniformity will involve either physical or theoretical over-lapping of adjacent swaths.
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
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.
Christina Finlayson, Research Associate, Plant and Food Research
Peas grown for processing can be a notoriously variable crop both in terms of overall productivity and maturity at harvest. In turn, this can have a significant impact on paid yields and grower profitability.
Many factors are thought to contribute to variability in peas – some of which are beyond grower control and some of which can potentially be influenced through management.
In recent years we’ve worked to consider a few of these ‘manageable’ factors, and specifically, the effect of seed characteristics on early crop vigour and the response of crops to plant growth regulators (PGRs).
The aim of this work has been to identify practices that reduce plant-to-plant variability in the field and maximise paid yield returns for growers and processors.
In our talk we present results from a greenhouse trial looking at the effect of seed characteristics on early crop growth, and how that relates back to the previous pea seed crop. Preliminary results suggested that different-sized pea seed can result in big differences in early plant size and growth.
Different-sized pea seed can result in big differences in early plant size and growth
These early differences in productivity are seldom overcome, so getting a consistent ‘start’ is important in minimising future variability issues. However additional variability develops following establishment .
In the field, we have conducted several trials assessing the potential efficacy of a range of PGRs on flowering dynamics and pea yields. Across these trials we’ve seen few clear effects of the PGR types, rates and timings that have been tested.
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’.
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
