The aim of this paper is to analiyse crowdsource data in arable crops production in Croatia and to desribe the significance in crowdsorucing data in optimizing crops fertilization. The crowdsource format in this research was used during 4 year collection data on arable crops growing with 4 groups of collected data: 1. data on crops and yields, 2. fertilization with organic fertilizer, 3. harvest residues, 4. mineral fertilization. In total, data of 13,239 requests for soil analysis during 2018-2021 were collected. The level of cooperation of producers regarding the collection of data on production when submitting requests for soil analysis is satisfactory, but it could be significantly better, especially regarding data on the achieved yield of pre-crops, on the use of organic fertilizers, and on the management of harvest residues. About half of the producers plan a medium high yield, a quarter a very high yield and a quarter a relatively low yield. This is in accordance with established average soil fertility indicators and with the consequences of soil fertility degradation. By analyzing collected data together with data on soil fertility it is possible to optimize fertilization and the target yield on area with insufficient yield by improving fertilization plan, to determine the reasons of the very low yield on some plots and implement measures to neutralize production limiting factors, and to maintain a high yield level of successful production with the systematic improvement towards more sustainable, cheaper and more diverse fertilization. According to collected data the use of organic fertilizers is not sufficient, but also data on available amounts and quality of manures, other fertilizers, and crop residues management should be more successful collected. The largest number of producers carried out fertilization according to recommendations and collected data indicate an undoubted connection between the fertilization and the achievement of the target yield. There is a great potential for expanding the quality and scope of input data, it is necessary to build a more effective system of continuous authorized active connection of producers with a data collection system. It is desirable to conduct research on the motivation and willingness of producers to cooperate in the collection of better-quality data. Considering the large amount of data and possible multi-faceted mutual influences, the analysis of the collected data should certainly, in addition to regression models, be refined by the use of neural network models.