A comparison of two methods to predict the landscape-scale variation of crop yield

Landscape-scale variation is a source of information that increasingly is b eing taken into consideration in agricultural and environmental studies. Mo dels that encompass and interpret this variation in fields and across contr asting management practices have the potential to improve the landscape man agement of agroecosystems. Our objective was to compare the results of two approaches, analysis of covariance (ANCOVA) and state-space modeling, to de termine the factors affecting grain yield in three crop rotations [pea (Pis um sativum L.)-wheat (Triticum aestivum L.)-barley (Hordeum vulgare L.), ca nola (Brassica napus L.)-wheat-barley, and wheat-wheat-barley] at two sites in Saskatchewan, Canada. Crop rotations were established in adjacent 30 m x 80 m plots arranged in a randomized complete block with five replicates. Variables that were expected to affect resource availability and pest infes tations in wheat (second rotation phase) or barley (third rotation phase) w ere measured. Each sampling point was classified according to landscape pos ition as either a shoulder or footslope. Landscape position was considered as a cross-classified treatment along with crop rotation, and analyzed usin g ANCOVA procedures. State-space modeling was conducted on a single transec t connecting sampling points across all of the rotations and replicates at each site. ANCOVA frequently indicated that grain yield and other measured variables differed between landscape position across all rotations, or in a specific crop rotation. For example, grain yield, soil water content, soil N availability during the growing season, and the incidence of common root rot were higher in the footslopes than the shoulders in all of the crop ro tations at one of the sites. However, the landscape position effect for gra in yield was never fully explained by the landscape position effects detect ed for the other variables (e.g., higher soil water content in the footslop es did not correspond with higher grain yields in footslope positions at bo th sites). State-space modeling indicated that most of the measured variabl es contributed to the prediction of landscape-scale variation for grain yie ld in the pea-wheat rotation; whereas only leaf and root disease incidences explained landscape-scale variation in the wheat-wheat rotation. The selec tive omission of data indicated that state-space modeling was accounting fo r the varied importance of the predictors across the landscape; i.e., local ized response functions. The major reason that ANCOVA did not explain lands cape-scale variation of grain yield across the different crop rotations may be because it was unable to account for highly localized variation. Howeve r, there is evidence from other studies that the ANCOVA approach is appropr iate when the response functions explaining grain yield do not vary signifi cantly within the study area. This situation is most likely to occur in stu dies with smaller experimental areas. Future research conducted at scales r eflecting 'real world' field conditions (i.e., study units representative o f producer's fields) should consider the use of state-space modeling or alt ernative statistical techniques that are designed to address and predict th e complex and dynamic nature of landscape-scale processes. (C) 2001 Elsevie r Science B.V. All rights reserved.