By 2050, it is widely predicted that the world’s population will reach approximately 9.5bn people, 34% higher than today.

To keep pace with rising populations and income growth, global food production will have to increase by 70%, whilst simultaneously seeking to minimise adverse environmental impacts and reduce greenhouse gas emissions.

These challenges require innovative and more intelligent farming systems to maximise yield, minimise input waste, and reduce yield loss.

In recent years, the agriculture industry has been introduced to a wide-range of data-capturing technologies, from machinery telemetry, proximal and remote sensing, input applications, yield mapping, weather monitoring, multi and hyperspectral imagery, pest and disease pressures and Farm Management Systems software.

This year, I got an opportunity to spend almost six weeks with Ohio State University on a Fulbright Tech Impact placement, to study how they have been using digital agriculture there as a management tool.

The Fulbright Tech Impact award is a research grant for Irish citizens to work on non-commercial research in the US for up to two months.

The idea is to look at technology’s impact in a particular field. When I arrived in mid-June the big talking point was the weather and how wet it had been for the entire spring season. At that point, less than 50% of the corn (maize) and 28% of the soya beans had been sown in Ohio.

Land not planted

By the time we got past the sensible sowing time of early July, only about 60% of the corn and 64% of the soya beans were sown and some of this was done in very poor soil conditions.

Crop insurance will be used for some of the non-sown areas and this will cushion some of the yield/revenue losses on the poorly established crops.

However, growers still had to come to some agreement with seed merchants for seed ordered and not used (up to 98% seed returned in some regions) and for the starter fertilisers which were specifically ordered and prepared for the sowing period.

The OSU programmes

Ohio State University (OSU) has been running a digital agriculture programme for many years.

It started as a precision agriculture programme and evolved into a digital agriculture programme. In 2018, they ran trials covering over 5,600ac on 95 farm research sites across the state.

Currently, OSU have a relationship with Case New Holland Industrial, whereby CNHi supply new equipment for the work (tractors, drills, cultivation equipment, sprayers and combines) each year free of charge.

The university is responsible for the operation of the equipment and CNHi puts a cap on the number of hours the tractors or combines can do.

This equipment is then sold by CNHi as demonstration equipment at the end of the season. Other companies such as Trimble, Precision Planting and Kubota also provide equipment for use by the college.

The university carries out trials in corn, on topics ranging from hybrid planting, N timing and management, N source and types and row spacing. In soya beans, there is a greater focus on fungicide timing, pest management and seeding rates.

This year there is some work being done on malting barley, driven by recent micro-brewery developments looking to source their barley locally.

Farms and systems

Most of the agriculture in the Ohio region around Columbus is tillage, with only a small amount of livestock or grass production.

Typical farm size is 1,500ac but there are a large number of farms over 4,000ac and some at 10,oooac-14,000ac. The grassland farms tend to be smaller and that brings down the average farm size.

The crops grown are mainly corn and soya beans in a very tight two-year rotation. Small grains such as wheat are not profitable by comparison.

When wheat is grown it is harvested in June to enable soya beans to be sown and harvested later in October, ie double cropping.

Following the harvest of beans or corn, ground is usually chisel ploughed to break up compaction. This is left fallow over winter and cultivated with a tine or disc cultivator in spring, prior to sowing with a precision planter.

Some of the analysis of weather and work windows has shown that, on average, the spring planting and autumn harvesting windows have been reduced by five days each over the last 15 years.

With these narrower windows plus increasing scale, growers have responded by investing in bigger equipment to get through the increasing workloads in a shorter time frame.

This has required drills that can cope with high forward speeds with increased tractor power to pull them.

These modern seed drills are pulled by big tractors at high speed and they must be designed to cope with both.

However, this has led to increased compaction and OSU are trialling options using different tyre/track combinations, moving towards smaller equipment where possible.

The farmers that I met were participating in the OSU research programme. This meant their farms were being used for trialling, which was giving them access to information that they might not get otherwise.

It also gave them opportunities to have discussions with key researchers on an informal basis.

Ohio has a big extension service to help with open days and trial events. The open days are very well attended, as there is a lot of interest in what OSU are doing.

A lot of their work is very practical, farmer focussed and hence well received by growers.

Data collection

For US growers, the data collection starts at sowing. The planters are sophisticated pieces of equipment with multiple sensors, which measure soil moisture, temperature, furrow consistency and residue levels.

The different pieces of equipment used, including this sprayer, can share the information gathered during their operation.

These sensors can, in real time, adjust each individual planting row unit and improve the cleaning of the furrow by adjusting the cleaning wheels, or increase or decrease the depth of sowing to ensure that the seed is placed at the optimal depth for rapid germination.

The data feed from these sensors is used with a soil organic matter (OM) map to increase the seeding population in areas of higher organic matter and to decrease the starter fertiliser rate in those higher OM areas.

All the data is collected using a terminal in the tractor cab and either sent by 4G directly to the farm or advisor’s office, or else it’s downloaded via Wi-Fi when the tractor comes back within range of the yard.

Inter-compatibility

While there are a number of different companies involved in providing technology, all these components are able to share data and interact in real time.

For example, the third party GPS mapping will update the in-cab terminal with location and speed; the pre-loaded OM map can be overlaid with the location data; the planting sensor can be given a signal to alter seeding rate and/or fertiliser rate; the in-furrow sensor can respond to these instructions and record the ‘as-applied’ treatment.

This drone had a sampling device suspended to take grab-samples from the maize crop.

OSU use third party software to read the data and link all the different streams together to create the ‘as-applied’ maps. They also link into the tractors CAN Bus signal to get GPS, speed and fuel data, also by using third party components.

Trimble software is typically used on combines and sprayers, as Trimble is the preferred supplier when a buyer requests yield monitoring or auto steer.

The Trimble data is also shareable across the rest of the platforms used at OSU – right down to the ATVs that are used to do scouting and soil mapping.

While the university is using state-of -the-art equipment, so are a number of the bigger growers. The technology is trickling down at all levels. There are a lot of growers using some level of digital or precision ag technology.

This interoperability of various pieces of the precision/digital technology is the key to OSU ability to provide good advice to growers. It is also important in enabling growers to be comfortable with investing in technology, once they know that it’s compatible across their equipment range and needs.

Messages for everyone

It is estimated that a typical grower in Ireland makes about 100 crop decisions across the growth cycle of a winter crop. It’s difficult to ensure that you have all the necessary data and information on hand to make the best decision at each point. UCD, like OSU, are working on the development of decision support tools to help growers with each decision along the growth cycle.

Take variety selection as an example. A grower needs to be able to make a decision based on; his/her soil type, date of sowing, crop rotation, weed pressure in-field, cultivations to be used, historical and current weather profiles, soil moisture, pH, soil temperature, germination rates, disease resistance, standing ability, straw quality, grain quality parameters, the market, the NPK strategy for the crop, expected disease management strategy based on crop resistance/field disease pressure, variety availability and seed dressing.

To make the best variety decision, the grower needs up-to-date data and localised data on the performance of the available varieties.

Decision support systems help growers make the optimum decision without overwhelming the grower with data.

Therefore, as seen with OSU, the interoperability of the various data streams and collecting tools is critical so that the as-sown map can be overlaid or compared with the fertiliser map, the disease treatment map and the yield map for the same location and year, so that year-on-year comparisons can be made.

These are the comparisons that OSU use when presenting data to growers.

Crop information and data will be the key tools for growers to enable them to respond to variability in crops in order to maximise profits, reduce wastes/overlaps and protect environmental quality.

If we can’t measure all aspects of our crop performance accurately, we have no idea if we are doing well or not and we can’t defend our sustainable agricultural production strategies.

There are lots of soil compaction issues in the US and we have similar problems in Ireland. Equipment is getting bigger and heavier, the working windows are getting tighter and there is pressure to get more done in a shorter time.

The next 10 years will see increased transition to smaller autonomous equipment, which will help deal with the compaction issue and the lack of skilled labour to work on farms.

However, for now, most machinery companies are just interested in selling ‘big iron’. It will be a long time before we begin to see those solutions.

But they are happening. The industry needs them, the technology is being developed and the transitions will occur.