But can it really capture all the locality-specific factors that are needed for good advice, the diversity of the farms and farmers, their labour constraints, their soil types, their skills and their market access? 

Let us assume that the ICRISAT app can work perfectly well – perhaps it will. Even then, it can be used to illustrate my second point. The app envisions to automatically guide farmers through the farm season. But do we really want this? Do we want farmers that merely follow commands, farmers that function like factory hands? Or do we want farmers who have the knowledge and skills to question such advice? Farmers that understand why some advice makes sense and then follow it, but who also have the knowledge and confidence to readjust, question and resist bad advice. One may say that this line of argument underestimates the capacities of farmers or that apps merely support decisions. But the line between supporting and making decisions may be thin.

Portraying m-services so blankly may seem extremely unfair, and I apologise for this. But portraying them as silver bullets may be equally unfair. If the history of agricultural development has told us anything, it is that there are no silver bullets. Admittedly, some m-services do have the potential to truly empower farmers (see also Daum, 2018). A farmer knowing the market price of the maize he or she wants to sell can bargain better than one who does not have this knowledge. A farmer knowing the local weather for the days to come clearly has an advantage – although truly local and reliable forecasts are still hard to come by. But the hype about m-services should not make us blind about their downsides. Some of them can easily be addressed, for example, by providing quality control and complimentary ICT-literacy.