In 2017 our 15th Annual Conference focuses on automated tools for data collection, decision making and doing actual tasks on the farm (and beyond).
What do you want?
What’s on offer?
How will farms and management have to change?
We have a comprehensive programme. We’ve gone a bit outside the box to bring a variety including from outside the horticultural and arable sectors. We find cross-pollination and hybrid vigour valuable!
So register, come along and listen to excellent presenters, discuss the ideas with colleagues and go away with new understanding and plans.
Now in year two of our OnionsNZ SFF project, we have trials at the MicroFarm and monitoring sites at three commercial farms in Hawke’s Bay and three more in Pukekohe.
A summary of Year 1 is on our website. A key aspect was testing a range of sensors and camera systems for assessing crop size and variability. Because onions are like needles poking from the ground, all sensors struggled especially when plants were small. This is when we want to know about the developing crop, as it is the time we make decisions and apply management.
By November our sensing was more satisfactory. At this stage we captured satellite, UAV, smartphone and GreenSeeker data and created a series of maps.
We used the satellite image to create canopy maps and identify zones. We sampled within the zones at harvest, and used the raltioship between November canopy and February yield to create yield maps and profit maps.
We also developed relationships between photographs of ground cover, laboratory measurements of fresh weight and leaf area and the final crop yield.
In reviewing the season’s worth of MicroFarm plot measurements and noticed there were areas where yield reached its potential, areas where yield was limited by population (establishment), some where yield was limited by canopy growth (development) and some by both population and development.
This observation helped us form a concept of Management Action Zones, based on population and canopy development assessments.
Our aims for Year 2 are on the website. We set out to confirm the relationships we found in Year 1.
This required developing population expectations and determining estimates of canopy development as the season progressed, against which field measurement could be compared.
We had to select our “zones” before the crop got established as we did a lot of base line testing of the soil. So our zones were chosen based on paddock history and a fair bit of guess work. Really, we need to be able to identify zones within an establishing or developing crop, then determine what is going on so we can try to fix it as quickly as possible.
In previous seasons we experimented with smartphone cameras and image processing to assess canopy size and relate that to final yields. We are very pleased that photographs of sampling plots processed using the “Canopeo” app compare very well with Leaf Area Index again this season.
Through the season we tracked crop development in the plots and using plant counts and canopy cover assessments to try and separate the effects of population (establishment) and soil or other management factors.
We built a web calculator to do the maths, aiming for a tool any grower or agronomist can use to aid decision making. The web calculator was used to test our theories about yield prediction and management zones.
ASL Software updated the “CoverMap” smartphone application and we obtained consistent results from it. The app calculates canopy ground cover and logs data against GPS position in real time. Because we have confidence that ground cover from image processing is closely related to Leaf Area Index we are working to turn our maps into predictions of final yields.
The current season’s MicroFarm crop is certainly variable. Some is deliberate: we sat the irrigator over some areas after planting to simulate heavy rain events, and we have a poorly irrigated strip. We know some relates to different soil and cover crop histories.
But some differences are unexpected and so far reasons unexplained.
Together with Plant and Food Research we have been taking additional soil samples to try and uncover the causes of patchiness.
We’ve determined one factor is our artificial rain storm, some crop loss is probably runoff from that and some is historic compaction. We’ve even identified where a shift in our GPS AB line has left 300mm strips of low production where plants are on last year’s wheel tracks!
But there is a long way to go before this tricky crop gives up its secrets.
Mark Redshaw put hours into getting the MicroFarm up and running and spending much of his free-time spraying and monitoring onions for two seasons. Now we have our own small sprayer we have taken that task over, but remain most grateful to Mark.
After a number of years of constant pea crops, we are having a break. Our main focus this season has been on onions, crop variability and its drivers. We have plenty of variability, but which factors are driving still proves elusive.
We do know topography and drainage are critical factors but they do not explain all the variation we are seeing. To assess their impact, we deliberately applied “heavy rain” to some areas and have been comparing these with areas not subjected to a hard40+mm rain event before emergence.
We prepared an OptiSurface plan two years ago but did not implement it as we were keen to explore variation in our onions trials. Perhaps it is time to act on our own advice!
The other main crop this season is sweetcorn. We are hosting a series of variety trials and are assessing a soil amendment product to see if it offers an economic advantage to growers.
To assess the soil amendment we set up a six plot replicated trial – with and without the treatment. We randomly split plots to avoid bias, and are taking crop development data through the season. At harvest we will determine paddock yield and the recovery rate of kernels in each plot.
The crop at the MicroFarm is showing increasing variability. The cause of some is understood, essentially excessive water pre-germination. But in some poor performing areas the causes have yet to be determined.
The effect of our artificially applied rain event pre-emergence is clearly evident in late November.
However, we also see other areas that have poor crop development that are outside the area irrigated to create the artificial rain event.
Sharp differences in crop growth are evident in the new onion ground. Some parts that were heavily irrigated to simulate heavy rain show reasonable development. Areas that were not irrigated also show good development, but in some patches total crop loss.
Investigations of soil physical properties in these different areas are underway.
After identifying areas within paddocks that had yields limited by different probably causes, we conceived the idea of Management Action Zones (MAZs).
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”.
The impact of the artificial rainstorm is evident on images taken at the end of November.
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.
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.
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.