The World AgriTech Innovation Summit is a great event. Start-ups, OEMs, agribusinesses and VCs all rub shoulders for two days with the intention of identifying the next big technology breakthroughs for precision agriculture.
This would seem no easy task, after all, each of the entities listed above has different objectives and views on the world – some very disparate. But it was heartening to hear general agreement on one topic: the desire to maintain yields while reducing inputs.
Weak commodity prices for the last three years have taken the growers’ focus away from ever increasing yields (why produce more when you drive the price ever lower in doing so?) and turned it to reducing the cost to produce a fixed number of bushels per acre.
Costs come in many different forms; fertilizer, fuel, fungicides, pesticides, and now increasingly, data. But data, or more specifically, information, holds the key to reducing the volume and hence cost of the major operational expenditure items.
Take the application of fertilizer for example. The amount you need to apply is dependent on two things; the amount of nutrients currently present in the soil and the expected uptake from the crop, typically driven by its growth stage. Better information on both will allow a grower to judge where and how much to add to the soil, optimising the input in order to achieve the desired yield per acre.
This is where sensors have a big part to play. Greater information about local soil conditions at the row-by-row level could enable growers to vary the rate of application on a row-by-row basis, rather than treating a whole field as a single entity, as is the case today. Furthermore, the greater level of granular information obtained from the sensors would allow a grower to move away from the current situation of ‘worst case application’ where a desire to avoid underdosing any part of the field leads to general overdosing as general assumptions are made as to the minimum likely nutrient content of any part of the field. A network of low cost, connected soil sensors could significantly reduce this behaviour by providing the required local information, building a real time ‘nutrient map’ of the field which could be uploaded to a variable rate sprayer, resulting in specific application only at points where it is required and at the optimum dose.
But the innovations don’t stop there. Once the crop has emerged from the soil and enters early to mid-stage growth cycles, machine vision technologies mounted on existing equipment could record the maturity stage of each individual plant, again building an incredibly rich dataset from which to make adjustments as to what nutrients to apply, where and how much, again on the row by row basis.
Both of these technologies borrow skills from adjacent industries to bring benefits that are unique the precision agriculture sector. It’s not quite doing more with less (an overused phrase anyway), but by achieving the same outcomes with dramatically reduced inputs, growers both help their bank balances and the environment in one fell swoop. In an increasingly polarised world that is something we can all agree on.