Abstract
Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.
Highlights
Estimating potential crop yield is a crucial activity performed in the assessment of seasonal production
This paper aims at the discovery and verification of long-term high and low yield productivity zones, as they are areas where crop yield has been for several years significantly above or below the average yield for the whole plot and because they require the site-specific tailoring of fertilizer application rates to ensure the most efficient use of nutrients for the determined level of yield production
Studies comparing predictions computed from satellite images with yield measurements at fully operational farms are rare since yield data are the most sensitive kind of farm data
Summary
Estimating potential crop yield is a crucial activity performed in the assessment of seasonal production It has been well established by works such as Auernhammer [1] that a plot is not a homogeneous area from the point of view of soil conditions, climate, or crop yield. Precision farming techniques count and rely on such heterogeneity All plots have their strong as well as weak zones from the crop yield point of view. Such indirect methods aim at estimations of yield, typically for a certain crop, area, and time Their common approach is to analyze climatic as well as soil conditions, agronomic practices, and crop growth characteristics [5,6,7]. Examples of methods for estimating the yield from the productivity zone and yield gaps may be found in Van Wart et al [11], Van Ittersum et al [12], and Chen et al [13]
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