Agrico is currently working on research into the application of nutrients, tailored to the requirements of individual potato varieties.
The company has a close co-operation with Yara Benelux and Tolsma-Grisnich for this purpose.
“Optimizing the absorption of nutrients is the first important cornerstone of this innovative development,” Agrico recently announced.
The second important cornerstone is the use of digital data from space, air, and soil. Combining customized solutions with digital data enables achieving higher yields, as well as a reduction in storage losses, and improved quality. Cultivation, storage, and processing are three topics that are important to making customized nutrient applications a success.
“We conduct long-term field trials in co-operation with Yara, both in the Netherlands (Kielstra) and in Germany (Yara). The photosynthesis activity of the plants is measured continuously during the growing season. The dry matter content in the foliage and tubers is also measured, and then the actual nutrient uptake is analyzed. This enables us to optimize the nutrient application for each variety, and minimize nutrient loss. During these tests, soil sensors constantly monitor the moisture and temperature levels. All this data is used to further optimize yield prediction models, amongst other things,” the experts added.
Storage and Processing
After harvesting, the potatoes from Agrico’s trial fields are stored for six months. During this period, the company’s experts and their chain partner Tolsma-Grisnich look at the volume loss during storage. Next, the partners assess the effect that the number of nutrients applied during the growing season has on the processing quality of the potatoes, which is why this is tested in the Agrico Quality Center each month.
“The results of all these analyses – during the growing season, storage and processing – are taken into account to optimize the nutrient application, tailored to the needs of individual potato varieties,” according to a recent press release.
Many activities are undertaken to collect and analyze data during the growing season, storage, and processing. This data serves as the basis for the further optimization of yield prediction models and customized nutrient programs.
