Modeling local terrain attributes in landscape-scale site-specific data using spatially lagged independent variable via
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Modeling local terrain attributes in landscape‑scale site‑specific data using spatially lagged independent variable via cross regression Terry Griffin1 · James Lowenberg‑DeBoer2
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract 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. Keywords Cross regression · Elevation · Landscape position · Lagged independent variable
Introduction The advent of global navigation satellite systems (GNSS) empowered farmers to test input choices before implementing farm management decisions across larger areas. Farmers are making decisions based on analysis of yield monitor data (Griffin et al. 2008). Data from yield monitors motivated the resurgence of on-farm experimentation because farmers could measure yield responses without interfering with harvest-time field operations (Griffin * Terry Griffin [email protected] 1
Department of Agricultural Economics, Kansas State University, Manhattan, KS 66506, USA
2
Harper Adams University, Newport, Shropshire, UK
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Precision Agriculture
et al. 2014). Recent studies estimate 39% and 68% of midwestern US farms have georeferenced yield data and automated guidance, respectively (Griffin and Yeager 2019; Miller et al. 2019). Farmers with GNSS-enabled yield monitors are likely to conduct landscapescale on-farm experiments (Griffin 2010). Technology-endowed farms are candidates for utilizing the analysis tools presented in this study. Farms with either GNSS-enabled yield monitors or automated guidance are likely to have access to elevation data sufficient to make use of these analyses. In addition, farms without high accuracy GNSS elevation data may have light detection and ranging (LiDAR) data available at near zero cost (Thomas et al. 2017). The overal
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