The phrase Agri-Tech is one that means many things to many people, and usually, most of them are right. This is a thought that struck home to me as I attended the Festival of Net Zero event run by Pershore College in late June. The event covered a broad range of projects and opportunities ranging from energy systems – data and technology, testing for disease, opportunities for robotics and the likelihood of some or all of these becoming commonplace over the coming years in our transition to improved carbon emissions without compromising productivity, and ideally without sacrificing profitability.

Data is a key area of focus in ensuring Agri-Tech is successful. Data informs the original decision for change and should be used to validate the decision thereafter. So often, we are concerned with how to best capture that data (what system, sensor, logging frequency, availability etc.), and while this is important, the key question that can be missed is what do you do with the data once it is collected? Good data analysis incorporates results and trend spotting with exception reporting as a minimum. However, the more data we get, the easier it is to get blinded by the sheer quantity and paralysed by how to visualise or handle it.

If you are considering gathering data, you can ask yourself the following questions to pinpoint how and what you might install:

1. What is the answer you are looking for? For example, do you want to know how much energy was consumed, what power something draws, how much airflow, etc., and what would deem a successful outcome?
2. Consider whether these items can be measured directly or whether you will need to use proxy measurements to predict the effects. In the examples above, power meters could give you energy, but if you are attempting to measure airflow at a leaf level, then it’s unlikely that you’ll get a sensor to measure that directly, and you may have to use temperature or something else entirely.
3. What interval is important? If you only want to know energy consumption daily, then looking at 30-second data will be irrelevant. However, if you are spotting issues with airflow, then daily average airspeed will be useless.
4. Can any existing systems (such as greenhouse climate control computers) be used to measure and gather the required data?
5. What are you going to use to handle the data? MS Excel is a good system, but data handling can be limiting, and visibility of results may be better done in something else? 

With these thoughts in mind, you will be able to create a plan to determine the sensor locations, the data logging frequency and crucially how you are going to get access to your data. If you don’t have existing systems suitable for gathering the information you need to be more efficient or inform decision-making, financial support may be available to help install equipment. The event discussed that Local Enterprise Partnerships often have funding available (match and grant), there could be a local Agri-Tech fund, Innovate UK may be willing to support a particularly innovative project and R&D tax credits can also help to reduce financial burden.