Abstract
Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha−1 for maize, 0.39 Mg ha−1 for cotton, 0.34 Mg ha−1 for soybean, and 0.59 Mg ha−1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.
Highlights
Precision agriculture aims to maximize the efficiency of the inputs supplied to a crop by defining zones to be managed homogenously within a field
The mean was negatively correlated with the standard deviation
In the synthetic dataset drawn from a left-skewed distribution, the correlation between mean and standard deviation was negative, whereas in the right-skewed distribution the correlation was positive, and it was null if the data were drawn from a symmetric distribution (Figure S3)
Summary
Precision agriculture aims to maximize the efficiency of the inputs supplied to a crop (e.g. fertilizer, herbicide, irrigation, etc.) by defining zones to be managed homogenously within a field. The farming-bysoil approach uses mapped soil properties and farmers’ observations (Fraisse et al, 2001; Mzuku et al, 2005) to delineate zones in which crops respond homogenously to inputs. This approach has often been criticized because it does not consider soil-climate interaction (Basso et al, 2007; Basso & Antle, 2020). The farming-by-yield approach (Basso et al, 2007; Basso et al, 2016; Blackmore, 2000; Lark, 1998) instead uses yield as a proxy for soil variables, and historical yield maps to define management zones. The farming-by-yield approach allows for the consideration of temporal stability in management; for example, by dividing the field into zones that have high mean productivity (high-and-stable), zones that have low mean productivity (low-and-stable), and zones that are temporally unstable, such as zones that have high year-to-year yield variability (Basso et al, 2007, 2019; Blackmore, 2000; Maestrini & Basso, 2018; Martinez-Feria & Basso, 2020)
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