PHOTO: HENK RISWICK
GLOBAL VIEW ▶▶▶ Putting a value on big data BY MATT MCINTOSH I
n the case of agricultural big data, some experts see the inability of developers to articulate value – specifically net savings and return-on-invest-
ment numbers – as one of the major barriers for adoption by farmers. Less-than-ideal big-data business models makes this even worse. “Data is raw material for information. Informa- tion is what can be used to make decisions, but information has value only if it influences decisions,” says Dr James Lowenberg-DeBoer at Harper Adams University (UK). Precision-ag technologies like GPS and autosteer, for exam- ple, have a comparatively immediate and obvi- ous economic return. Consequently, they be- came the new norm within a decade. Measuring big data tools like multi-layered soil maps are more complicated but it’s still possi- ble to articulate value. It becomes more diffi- cult for, say, info on harvest yields and quality. A complicating factor is the reliance on open pooled data. In the current landscape, most big-data systems are managed by private com- panies operating proprietary systems. This has many farmers concerned that farm data can be used to harm their business. For example, if a region harvests poor quality soy beans due to a frost, this knowledge can either be used to sell inferior product early with the aim to pre- vent storage losses for farmers. Alternatively a
Will harvest data solely benefit the farmers, or might it be sold to the buyers of their crop?
less benevolent grain buyer can drop the buy- ing price, and the farms lose. The feeling exists that much of the value of big farm data is beyond the farm gate.
Solutions in more refined models Data sharing business models could try to work past these difficulties in several ways: • Data systems developed with public pri- vate-partnerships. This would include govern- ments, universities, farm associations, and ag tech businesses not also selling inputs.
• Developing up front compensation mecha- nisms for data collection costs. • Facilitate farmer benefits for access down- stream, or developing ways farmers can more easily use pooled data to make their own decisions. • Make data collection easier. • Transparent, third party evaluation of data applications. • Clarify ownership issues, ideally, through legislation.
Penny wise, pound foolish BY LEO THOLHUIJSEN D 42
utch magazine Bo- erderij investigated the quality of soil sampling and soil analysis. Follow-
ing this, it also investigated the quality of fer- tilisation recommendations given by different laboratories, based on the analysed soil sam- ples. The results of this comparison are remark- able, not to mention disconcerting.
As it turned out, the Pw-number (phosphate fraction soluble in water) varied between 37 and 75. Yes, you are reading this correctly: One laboratory found twice as much phos- phate in the sample than the other. An enor- mous difference. The labs also have different opinions on the soil’s ability to supply nitro- gen. According to one lab this was only 45 kg per hectare. The other accredited labs put this number at 110 to 119 kg per hectare. Again, an enormous difference.
▶ FUTURE FARMING | 1 November 2018
Consistent results Naturally, this comparison is only a snapshot. You cannot simply conclude that one lab al- ways comes back with higher numbers than the other. However, that would hardly be an extenuating circumstance; the chaos and un- reliability would only increase. To my amazement, a supplier of soil scans, who is a major consumer of soil analyses to convert relative values to absolute values, gave a laconic answer to our findings. ‘What else is
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