Predicting yield of barley across a landscape: a state-space modeling approach Ole Wendroth a, * , Hannes I. Reuter a , K. Christian Kersebaum b a Department for Soil Landscape Research, Centre for Agricultural Landscape and Land Use Research, ZALF, Eberswalder Str. 84, 15374 Mu ¨ncheberg, Germany b Department of Landscape Systems Analysis, Centre for Agricultural Landscape and Land Use Research, ZALF, Eberswalder Str. 84, 15374 Mu ¨ncheberg, Germany Abstract Spatial crop yield prediction is an enigma that needs to be solved to avoid ecological and economical risks in agricultural crop production, that can result from local fertilizer surplus or deficiency. Current approaches for site-specific fertilizer distribution are based on patterns of soil properties and yield maps obtained from previous years. The aim of this study was to evaluate the quality of crop yield prediction in an arable field using two sets of variables in autoregressive (AR) state-space models. One set included detailed soil information (texture, organic carbon content) and yield data from the previous year at a high spatial resolution. In the other set, remotely sensed soil and crop information (vegetation index, crop nitrogen status, land surface elevation) was assembled, which is available under farm conditions without intensive soil sampling campaigns. Soil and remotely sensed variables were evaluated in bi- and multivariate autoregressive state-space analysis to predict spring barley grain yield. Remotely sensed variables showed to be better predictors for spatial grain yield estimation than soil variables. Transition coefficients determined from state-space analysis were applied in AR-equations with soil and remotely sensed information, but yet given only the initial value of the spatial yield series. Both sets of variables elicited similar prediction quality. q 2002 Elsevier Science B.V. All rights reserved. Keywords: Precision farming; Field-scale variability; State-space models; Land surface elevation; Autoregressive crop yield prediction 1. Introduction Modern agricultural production is characterized by highly intensive and efficient production systems, and the average field size has been increased tremen- dously over past decades. Nowadays, large field units are managed homogeneously, although there exists a considerable inherent soil spatial variability causing spatially differing zones of fertility and physical properties. When such large field sites are fertilized homogeneously for example with nitrogen (N), economical and ecological disadvantages can be the consequence. The first is the case when the applied fertilizer dose is below the local optimum, the second when the applied fertilizer cannot entirely be used by the crop and may cause leaching of fertilizer nutrients. Farmers in the last decades have already intuitively met decisions with respect to the spatial variability pattern within their fields or have been varying fertilizer application rates locally, based on their experience and their expectations. This idea has been embedded in a technology, i.e. precision or site- specific farming, which are now possible due to Journal of Hydrology 272 (2003) 250–263 www.elsevier.com/locate/jhydrol 0022-1694/03/$ - see front matter q 2002 Elsevier Science B.V. All rights reserved. PII: S0022-1694(02)00269-X * Corresponding author. Fax: þ 49-33432-82280. E-mail address: owendroth@zalf.de (O. Wendroth).