Agriculture is one of the most important sectors of the Australian economy, and it’s one that has been severely under-invested.
While the focus on sustainable agriculture has increased dramatically in recent years, research shows that it’s really not possible to achieve an agricultural system that’s sustainable without investing heavily in science and technology.
That means the key to an agriculture system that is sustainable is research and development, or R&D.
In recent years we’ve seen an increase in the number of university graduates in this field, with an increasing number of people getting degrees in sustainable agriculture, particularly in the US.
This has helped create a huge amount of R&d investment.
But in recent decades, the focus has shifted towards research and design and not on agriculture.
That’s where a new sector is emerging.
It’s called systematic agriculture.
A systematic agriculture is a system that combines research, design and engineering in a way that is scalable, economical and efficient.
It is the next generation of the agricultural research and the development of new technologies.
There’s a lot of potential for systematic agriculture to be very useful, but we need to be careful in our approach.
This new sector has created an opportunity to look at how we can combine R&dk to create a sustainable agriculture system, as the research is already there and the technologies already in place.
A system that can be applied across all of the sectors of agriculture The first problem is that it requires a lot more R&dl to develop an agricultural research system that achieves a sustainable agricultural system.
A lot of what is available in agricultural research is based on old research methods.
It doesn’t necessarily work the way you want it to.
It works the way it was done for a while, and we haven’t really been able to get the technology to be more flexible, to be better at predicting what the outcomes of a particular system will be.
A major problem is the number and variety of different agricultural systems, all of which have to be evaluated, tested and compared.
There are different ways of using different technologies.
We’re using a combination of different approaches to look and see what works, what doesn’t work, and what can be improved.
That makes it very difficult to evaluate what the results of the various approaches are.
We’ve found that the best systems are not necessarily those that we’ve tried the most.
The system is more than the sum of its parts.
It needs to be carefully integrated into the overall system, or the system will not be sustainable.
The second problem is just the sheer volume of research.
It takes time to get a system to a point where you can test it on the ground, on a large scale.
You need lots of data and it takes time, too.
There is also the issue of data protection.
In the US, a large amount of research is being done by the USDA (United States Department of Agriculture).
That data is shared across a number of government agencies, and there is a significant amount of data that has not been shared with researchers.
This is a problem because it’s important that all the data that’s shared be open, so researchers can see how it’s being used.
We don’t want to create systems that are closed off from the world.
We want to ensure that the data is kept safe, so that people can see the data and make informed decisions about it.
The third problem is what is called a ‘gold standard’.
In other words, the system is supposed to be a reference system.
The reference system will allow us to look back and see how the systems have performed, and the performance will be based on a set of performance metrics, such as the yield, the water use and the productivity.
These are all part of the standardised data that we collect from a variety of sources.
We know the system works and it works well.
So why are there so many different systems?
The first thing that needs to happen is that we have to establish what the standard of the systems that we’re looking at.
That will mean looking at the various types of systems.
For example, we need a system for cropland management that is based around a number, like 5 hectares, or 1,000 hectares.
This will help us to compare and contrast different systems, and hopefully we’ll find that we can achieve the system we want.
The next step is to look in more detail at the different crops and how they perform.
If we can see that these systems work, then we can then build the system based on those performance metrics.
For instance, if we can look at the yield of the crops, we can build a system based around the yield and compare that with the performance metrics we’ve set out.
We can also look at water use, the amount of water that is required to grow the crops.
We could look at productivity and compare it with other systems.
There could be more than one system to look into.
We also need to look