PILOT : CEREALS AND BIOMASS CROPS_4

The pilot aims to develop a platform for mapping of crop vigor status by using EO data (Landsat, Sentinel) as the support tool for variable rate application (VRA) of fertilizers and crop protection. This includes identification of crop status, mapping of spatial variability and delineation of management zones.

The Czech Republic has a specific land use defined by the highest average holding area in EU (over 130 ha per farm). The national statistical report of agriculture sector (“Green Report 2015”, Ministry of agriculture, Czech Republic) shows that there are 637 farms with acreage of managed land over 1000 ha which together cultivate 50.3% of agricultural land in Czech Republic. Also, there is known higher average size of fields. Statistical evaluation of the size of land parcels in LPIS shows that 60% of arable land is located within the fields with the area over 20 hectares. Higher diversity of the relief and pedoclimatic conditions in combination with the size of land blocks occur in visible heterogeneity of land. This leads to an increased interest in the precision farming practices and technologies for site-specific crop management, where remote sensing (EO) plays a crucial role.

The pilot farm Rostenice a.s. with 8.300 ha of arable land represents a bigger enterprise established by aggregating several farms in past 20 years. Main production is focused on the cereals (winter wheat, spring barley, grain maize), oilseed rape and silage maize for biogas power station. Crop cultivation is under standard practices, partly conservation practices is treated on the sloped fields threatened by soil erosion. Over 1600 ha is mapped since 2006 by high density soil sampling (1 sample per 3 ha) as the input information for variable application of base fertilizers (P, K, Mg, Ca). Farm machines are equipped by RTK guidance with 2-4 cm accuracy. Farm agronomists don’t use any strategy for VRA of nitrogen fertilizers and crop protection because of lack of reliable solutions in CZ.

The main expected results of this pilot are:

• Visualization of crop status by vegetation indices within selected time period.

• Identification of spatial variability of crops within each farm field and alerting service for farmers (in case that crop variability is higher than usually for that site).

• Zoning of fields by using machine learning algorithms for selected time-period and creating of prescription maps (incl. export into shp or isoxml) by considering restricted zones for application.

Three relevant KPI were identified for this pilot:

• Area of processed EO data

• Accuracy of management zones delineation by field survey and yield maps

• Increase of fertilizers use efficiency and farm productivity