Spatial and temporal variability of yield maps can localize field management -- a case study with corn and soybean

Authors: DE SOUZA, EDUARDO - WESTERN PARANÁ STATE UNIVERSITY; KHOSLA, RAJIV - KANSAS STATE UNIVERSITY; SUDDUTH, KENNETH; JOHANN, JERRY - WESTERN PARANÁ STATE UNIVERSITY; BAZZI, CLAUDIO - FEDERAL UNIVERSITY OF TECHNOLOGY - PARANA

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/3/2025
Publication Date: 5/13/2025
Citation: De Souza, E.G., Khosla, R., Sudduth, K.A., Johann, J.A., Bazzi, C.L. 2025. Spatial and temporal variability of yield maps can localize field management -- a case study with corn and soybean. Agronomy. 15(5). Article 1179. https://doi.org/10.3390/agronomy15051179.
DOI: https://doi.org/10.3390/agronomy15051179

Interpretive Summary: Spatial crop yield patterns are often affected by management and environmental factors, both of which may vary from year to year. This makes visual and statistical interpretation of long-term data more difficult. Various metrics are often used to interpret multi-year yield datasets, such as mean normalized yield and the temporal standard deviation (TSD) of yield. However, interpretation would be simplified if multiple aspects of yield variability could be combined into a single metric. Such a metric, the yield performance index (YPI) is introduced in this paper. The YPI increases with increasing yield and with decreasing TSD, meaning that a high YPI represents the more favorable conditions of higher yield and less temporal variation. Use of the YPI to identify field areas that need attention due to low yield and high variation is illustrated by its application to multi-year data from three fields.

Technical Abstract: Yield maps represent crop production output and are essential for evaluating within-field spatial variability. Managing this yield variability is a critical issue for precision and digital agriculture to facilitate optimized crop yield and reduced environmental impact. This work evaluated spatial and temporal variability of yield data from three conventionally-managed agricultural fields. The data variability was measured through standard deviation (SD) and coefficient of variation (CV%). After separately normalizing each year of yield data, the temporal variability (TSD and TCV%) was calculated by grid cell for each field across years. A new index is proposed in this paper, the yield performance index (YPI, the ratio of mean normalized yield to the TSD), as an index that which has a lower value for lower yield and higher temporal variability. The YPI was a valuable tool for analyzing yield maps and looking for areas that need particular attention, both areas with consistently high yields and low variability and areas with low yields and high variability.