Monitoring Water Banks with Telemetrics

Longstanding droughts across South Australia are forcing farmers to rethink the moisture in the soil they once regarded as their inalienable right. Trend monitoring is an essential input to applying pesticides and fertilisers in balanced ratios. Soil moisture sensors are transmitting data to central points for onward processing on a cloud, and this is making a positive difference to agricultural output.

Peter Buss, co-founder of Sentek Technology calls ground moisture a water bank and manufactures ground sensors to interrogate it. His hometown of Adelaide is in one of the driest states in Australia. This makes monitoring soil water even more critical, if agriculture is to continue. Sentek has been helping farmers deliver optimum amounts of water since 1992.

The analogy of a water bank is interesting. Agriculturists must ?bank? water for less-than-rainy days instead of squeezing the last drop. They need a stream of online data and a safe place somewhere in the cloud to curate it. Sentek is in the lead in places as remote as Peru?s Atacamba desert and the mountains of Mongolia, where it supports sustainable floriculture, forestry, horticulture, pastures, row crops and viticulture through precise delivery of scarce water.

This relies on precision measurement using a variety of drill and drop probes with sensors fixed at 4? / 10cm increments along multiples of 12? / 30cm up to 4 times. These probe soil moisture, soil temperature and soil salinity, and are readily re-positioned to other locations as crops rotate.

Peter Buss is convinced that measurement is a means to the end and only the beginning. ?Too often, growers start watering when plants don’t really need it, wasting water, energy, and labour. By monitoring that need accurately, that water can be saved until later when the plant really needs it.? He goes on to add that the crop is the ultimate sensor, and that ?we should ask the plant what it needs?.

This takes the debate a stage further. Water wise farmers should plant water-wise crops, not try to close the stable door after the horse has bolted and dry years return. The South Australia government thinks the answer also lies in correct farm dam management. It wants farmers to build ones that allow sufficient water to bypass in order to sustain the natural environment too.

There is more to water management than squeezing the last drop. Soil moisture goes beyond measuring for profit. It is about farming sustainably using data from sensors to guide us. ecoVaro is ahead of the curve as we explore imaginative ways to exploit the data these provide for the common good of all.

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A Definitive List of the Business Benefits of Cloud Computing – Part 2

Improves cash flow

The capital investment you put into an on-premise IT infrastructure is normally based on a long-range forecast of what your highest computing demands will be. But what if, as they often do, the estimates turn out to be too high? Then you’ll have to bear with the huge depreciation cost or monthly amortisation of a grossly underutilised asset for the next couple of years. (more…)

Energy Cooperation Mechanisms in the EU

While the original mission of the European Union was to bring countries together to prevent future wars, this has spun out into a variety of other cooperative mechanisms its founders may never have dreamed of. Take energy for example, where the European Energy Directive puts energy cooperation mechanisms in place to help member states achieve the collective goal.

This inter-connectivity is essential because countries have different opportunities. For example, some may easily meet their renewable targets with an abundance of suitable rivers, while others may have a more regular supply of sunshine. To capitalise on these opportunities the EU created an internal energy market to make it easier for countries to work together and achieve their goals in cost-effective ways. The three major mechanisms are

Joint Projects
Statistical Transfers
Joint Support Schemes

Joint Projects

The simplest form is where two member states co-fund a power generation, heating or cooling scheme and share the benefits. This could be anything from a hydro project on their common border to co-developing bio-fuel technology. They do not necessarily share the benefits, but they do share the renewable energy credits that flow from it.

An EU country may also enter into a joint project with a non-EU nation, and claim a portion of the credit, provided the project generates electricity and this physically flows into the union.

Statistical Transfers

A statistical transfer occurs when one member state has an abundance of renewable energy opportunities such that it can readily meet its targets, and has surplus credits it wishes to exchange for cash. It ?sells? these through the EU accounting system to a country willing to pay for the assistance.

This aspect of the cooperative mechanism provides an incentive for member states to exceed their targets. It also controls costs, because the receiver has the opportunity to avoid more expensive capital outlays.

Joint Support Schemes

In the case of joint support schemes, two or more member countries combine efforts to encourage renewable energy / heating / cooling systems in their respective territories. This concept is not yet fully explored. It might for example include common feed-in tariffs / premiums or common certificate trading and quota systems.

Conclusion

A common thread runs through these three cooperative mechanisms and there are close interlinks. The question in ecoVaro?s mind is the extent to which the system will evolve from statistical support systems, towards full open engagement.

Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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