What should be corrected or removed during the yield data cleaning process?

During the yield data cleaning process, it is essential to identify and remove outlier data that may skew management decisions. Outliers are data points that significantly differ from the rest of the dataset, potentially due to measurement errors, unusual events, or inconsistencies in data collection. Leaving these outlier data points in the dataset can lead to incorrect analyses and misguided decisions, such as improper assessments of crop performance or misallocation of resources. By removing or correcting these outlier data points, analysts can ensure that the resulting dataset accurately reflects the true yield and provides a reliable basis for effective agricultural management practices.

In contrast, accurate and reliable data, verified data from multiple sources, and consistent yield points across regions typically enhance the integrity of the dataset. These elements are critical for conducting valid analyses and making informed decisions in agricultural operations. Thus, they should not be removed during the data cleaning process.