Spatial and Temporal Variability in Yield Maps Can Localize Field Management—A Case Study with Corn and Soybean

Yield maps represent crop production output and are essential for evaluating within-field spatial variability. Managing this yield variability is critical for precision and digital agriculture to facilitate optimized crop yield and reduced environmental impact. This work evaluated the spatial and temporal variability in corn and soybean yield data from three conventionally managed agricultural fields, with nine, three, and four seasons’ data. The data variability was measured through standard deviation (<i>SD</i>) and coefficient of variation (<i>CV%</i>). After separately normalizing each year of the yield data set, the temporal variability (<i>TSD</i> and <i>TCV%</i>) was calculated by grid cell for each field across years. A new index is proposed in this paper, the yield performance index (<i>YPI</i>, the ratio of mean normalized yield (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mover accent="true"><mrow><mi>Y</mi></mrow><mo stretchy="false">¯</mo></mover></mrow><mrow><mi>N</mi></mrow></msub></mrow></semantics></math></inline-formula>) to the <i>TSD</i>), as an index with a lower value for lower yield and higher temporal variability. Two, three, and four zones were delineated using only <i>YPI</i>. These zones were valuable for identifying areas needing particular attention, with consistently (i) high yields and low variability or (ii) low yields and high variability.