The purpose of the research is to find the suitable area where new agricultural innovations are profitable for farmers while concurrently identifying and assessing the multivariate factors exerting influence on farmers' profitability on a global scale. Thus, we are conducting a survey where we plan to collect global data on a range of agricultural innovations, which we will subsequently use for academic research purposes. For this research, we need to understand the locations where these innovations are already being used and a couple of answers about them, especially whether they are profitable or not. The provided information will be used to train state-of-the-art machine learning algorithms to create global maps that show the profitability of the different innovations.

 Methodology: We have developed a short online survey, which we sent to experts from academia, companies, farming, and various other organizations and backgrounds. We will use these data points to train machine learning models to predict globally where the different technologies (precision agricultural maps, smart irrigation, weeding robots, etc.) are likely profitable at the moment and where they are not. This will help make agriculture more productive and sustainable.

 Abstract

Mechanization is a key ingredient for raising agricultural productivity. Currently, however, globally comparable information on agricultural mechanization rates is hard to come by. Here, we globally model agricultural mechanization at an unprecedented ~ 5km resolution and examine its empirical association with crop yield gaps but also soil compaction and forest loss. We find that many high-income countries are close to fully mechanized by now, whereas, especially in Sub-Saharan Africa and Southern Asia, mechanization rates are still low. We then estimate that for each ten-percentage point increase in the mechanization rate, the average crop yield gap shrinks by 2 to 3 percentage points and soil compaction increases by 1.5 to 2 percentage points. There is no clear effect of agricultural mechanization on tree cover loss.