J. L. Ping

TL;DR: In this article, different geostatistical techniques for mapping organic carbon stock in the top 0.3 m of soil with or without secondary information were assessed in three large no-till fields (49 to 65 ha) in Nebraska.

TL;DR: In this paper, three or four yield categories were defined using empirically defined yield categories or through hierarchical or non-hierarchical cluster analysis techniques and compared using average yield, average yield and its standard deviation (MS), or all individual years (AY) as input variables.

TL;DR: In this article, a general procedure for assessing yield data cleaning methods was applied to a new postprocessing algorithm in which six common types of erroneous yield monitor values were removed: (1) combine header status up; (2) start/end-pass delays; (3) grain flow, distance traveled, and grain moisture outliers; (4) values exceeding minimum and maximum biological yield limits; (5) local neighborhood outliers, and (6) short segments and co-located points.

TL;DR: In this paper, different geostatistical procedures for creating interpolated yield maps by integrating yield data with remotely sensed vegetation indices (VI) were evaluated, and the results showed that SKLM performed best in terms of the precision of grain yield maps and maps that depicted true yield patterns.

TL;DR: In this article, the authors evaluate the application of both global and local spatial association statistics to explore the spatial dependence of cotton (Gossypium hirsutum) yield and yield pattern changes under two weather scenarios, and compare effects of weight selection on spatial autocorrelation statistics.