Yield Monitor Data Analysis Protocol: A Primer in the Management and Analysis of Precision Agriculture Data

Griffin, Terry; Brown, Jason P.; Lowenberg-DeBoer, Jess

doi:10.2139/ssrn.2891888

Cited by 17 publications

(20 citation statements)

References 6 publications

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“…To construct a data set containing both yield and soil texture, the aggregating procedure described by Griffin et al (2007) and Vories et al (2015) was followed. The spatially referenced textural data developed from the EC a survey was used and all yield data points within 1.8 m of an EC a and associated estimated sand content data point were averaged.…”

Section: Methodsmentioning

confidence: 99%

“…The large, spatially referenced data sets were managed with ArcGIS for Desktop 10.4.1 (ESRI, Redlands, CA, USA) and spatial analysis of variance (SANOVA) was conducted using GeoDa 1.8.16 (Center for Spatial Data Science, Univ. of Chicago, Chicago, IL, USA) using the spatial error model as recommended for yield monitor data (Griffin et al 2007). SAS for Windows 9.4 (SAS Institute Inc., Cary, NC, USA) was used for the linear regression of the EC a data.…”

Section: Methodsmentioning

confidence: 99%

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Comparison of precision and conventional irrigation management of cotton and impact of soil texture

et al. 2020

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Soil textural variability diminishes the effectiveness of conventional irrigation management. Variable rate irrigation (VRI) can address soil variability; however, users need guidance to prepare prescriptions for optimal water application. A study was conducted at Portageville, MO, USA, in 2016 and 2017 with the objective to compare yield and irrigation water use efficiency among three water-management treatments for cotton: rainfed, irrigated based on the USDA-ARS Irrigation Scheduling Supervisory Control And Data Acquisition (ISSCADA) system, and irrigated based on a water balance method. Sand content in the top 533 mm soil layer was estimated from apparent electrical conductivity (EC a). Yield values measured near an EC a observation were averaged to create a data set containing sand content and associated yield. Although the trend was for the rainfed treatment to have the lowest yield in both years, the yield differences among all treatments were not significant when sand content was not considered. A strong effect of sand content on cotton yield was observed in both seasons, although the slopes differed among the water management treatments in 2016. The ISSCADA system tended to have a higher irrigation water use efficiency in both seasons, but the difference was not significant in 2016 when total irrigation applications were low. The study is continuing at Portageville and other locations and the ISSCADA system is constantly being improved to better meet the needs of agricultural producers.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparison of precision and conventional irrigation management of cotton and impact of soil texture

et al. 2020

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“…Aspatial, cross-regressive, and SEM models were estimated to analyze field-scale site-specific data. Yield monitor data were cleaned to remove erroneously measured observations and to relocate points to correct locations per procedures suggested by Griffin et al, (2007) and Sudduth et al (2012). Aspatial and cross-regression analyses were estimated as OLS.…”

Section: Methodsmentioning

confidence: 99%

Modeling local terrain attributes in landscape-scale site-specific data using spatially lagged independent variable via cross regression

Griffin

Lowenberg‐DeBoer

2019

Precision Agric

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Analysis methods for landscape-scale site-specific agricultural datasets have been adapted from a wide range of quantitative disciplines. Due to spatial effects expected at landscape scales with respect to yield affecting factors, inference from aspatial analyses may lead to inefficient statistical inference. When spatial correlation exists within a random variable e.g. explanatory variables such as elevation or soil characteristics, spatial statistical methods can provide unbiased and efficient estimates on which to base economic analyses and farm management decisions.Simple continuous terrain variables derived from spatially lagged independent variable transformation of relative terrain position allowed models to be estimated using familiar linear aspatial models without introducing the problems associated with interpolated data in inferential spatial statistics. Using site-specific data from three example fields, cross regressive elevation variables complemented topographic attributes, rather than replacing them in a range of statistical models. Results indicated that cross regressive elevation variables, especially relative elevation, reduced estimation problems due to correlation among independent variables and bias arising from spatially interpolated data in statistical analysis.

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“…One paramount farm data layer has been instantaneous yield monitor data from global navigation satellite system (GNSS) equipped combine harvesters (Griffin et al, 2004). Considerable effort has been made to replace human intervention of cleaning erroneously measured data (Drummond, 2012;Griffin, Brown, and Lowenberg-DeBoer, 2007) with automated processing, especially for cloud computing (Griffin, 2018).…”

Section: Farm Data Valuationmentioning

confidence: 99%

Estimating Value, Damages, and Remedies When Farm Data Are Misappropriated

Miller¹,

Griffin

Goeringer

et al. 2018

SSRN Journal

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Yield Monitor Data Analysis Protocol: A Primer in the Management and Analysis of Precision Agriculture Data

Cited by 17 publications

References 6 publications

Comparison of precision and conventional irrigation management of cotton and impact of soil texture

Comparison of precision and conventional irrigation management of cotton and impact of soil texture

Modeling local terrain attributes in landscape-scale site-specific data using spatially lagged independent variable via cross regression

Estimating Value, Damages, and Remedies When Farm Data Are Misappropriated

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