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.

了解子田作物产量和时间稳定性对于更好地管理作物至关重要。已经提出了几种算法来研究场内时间变异性，但它们大多限于少数几个领域。在这项研究中，使用由农民提供的 768 个田地的 5520 张产量图组成的大型数据集，用于研究子田产量分布偏度对时间变异性的影响。这些数据用于测试两种用于映射稳定性的直观算法：一种基于标准差，第二种基于像素排名和百分位数。对产量监测数据的分析表明，产量分布是不对称的，并且对于所分析的所有四种作物来说，它往往是负偏态的（p < 0.05），这意味着低产地区的频率较低，但覆盖的低值范围更大.^玉米为-1 ，棉花为0.39 Mg ha ^-1 ，大豆为0.34 Mg ha ^-1 ，小麦为0.59 Mg ha ^-1。不稳定区域的产量与标准差算法分类为低稳定的像素相似，而双向异常值算法没有表现出这种偏差。此外，可用产量图年数的增加导致稳定性分类的确定性适度但显着增加，并且不稳定像素的比例随着包含产量图的年份之间的降水异质性而增加。