Variability in regional wheat yields as a function of climate, soil and economic variables: Assessing the risk of confounding

By M. Bakker, Gerard Govers, Frank Ewert, Mark D. Rounsevell, Robert J. A. Jones. 2005

90 citations recorded by the source corpus.

Mechanisms that explain spatial variability and trends in agricultural productivity at the regional scale are not well understood. Statistical approaches may be used to relate crop yields and trends in crop yields to changes in the economic and bio-physical environment. However, potential yield-explaining variables tend to confound at the regional scale due to strong correlations between these variables, which complicates the interpretation of such empirically derived relationships. In this paper, we assess relationships between different physical and economic variables and yields and trends in yields at the regional scale along a climatic gradient in Europe. We assess the extent of confounding (i.e. confusing the roles of different variables due to strong correlations) among these variables and the associated risk for explaining yield variability and trends in yields. We analyze regional wheat yield data at NUTS3 and NUTS2 level for most of the EU-countries. Soft wheat (Triticum aestivum L.) is chosen as an indicator crop as it is grown over a wide climatic gradient, and its physiology has been subject to many agronomical studies. Time series were used to derive trends in yields. Data of important climatic, soil and economic variables were also derived at NUTS3 and NUTS2 level. Correlation coefficients were calculated between these variables and yields and yield trends. Confounding was assessed by comparing the R-2 values of the regression models with and without (groups of) variables. Soft wheat productivity could be described to a very satisfying level. High R-2 values were obtained, partly due to aggregation of spatially autocorrelated in- and output data. High correlations were found between all variables, which indicates a risk of misinterpretation of results from statistical models when only few (groups of) variables are considered in the analysis. At a higher aggregation level (NUTS2) both the model fit and the risk of confounding increase. Validation by an independent dataset does not lead to exclusion of confounding. This paper serves as a basis for further research on spatial variability and trends in agricultural productivity at the regional scale, indicating both the possibilities and the risks of such research. (c) 2005 Elsevier B.V. All rights reserved.

Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/f48ce335489015ad8fca933f39b80c240b6c0d95

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Citations in the corpus

Impact of heat stress on crop yield—on the importance of considering canopy temperature (2014, methodology): “Previous work, has used statistical methods to analyze the relationship between high temperature and crop yields for different crops and regions [15, 20], though such methods are not able to identify nor explain the specific processes responsible for yield losses [16, 25].”
Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. (2009, methodology): “Climate model output can be used with crop models either directly (Mavromatis and Jones, 1999; Challinor et al., 2005a, b, c) or via some post-processing.”
Analysis of farm performance in Europe under different climatic and management conditions to improve understanding of adaptive capacity (2007, background): “However, recent studies suggest that soil characteristics explained only little of the spatial variability in wheat yields across Europe (Bakker et al. 2005) and significant effects on farmers’ income were not observed in other regions (Liu et al. 2004).”
Vulnerability and adaptation of European farmers: a multi-level analysis of yield and income responses to climate variability (2009, background): “However, results from these analyses are constrained by the risk of confounding (e.g., Bakker et al. 2005), especially when non-climatic factors are omitted as frequently happens.”
Understanding variability in soybean yield and response to P-fertilizer and rhizobium inoculants on farmers’ fields in northern Nigeria (2016, background): “Bakker et al. (2005) found an R2 of about 0.90 for the relationship between yield data (10 year average of regions in Europe) and soil, climate and economic variables.”
Is Cassava the Answer to African Climate Change Adaptation? (2012, background): “…at all (Boote et al., 2010, 1996; Challinor and Wheeler, 2008a), and partly because most research on climate change impact assessment has focused on the better documented staples maize, wheat and rice (Aggarwal and Mall, 2002; Bakker et al., 2005; Jamieson et al., 2000; Jones and Thornton, 2003).”
The destination of arable land in a marginal agricultural landscape in South Portugal: an exploration of land use change determinants (2007, background): “…with the set of descriptive variables Furthermore, when the sum of chisquares of models two and three is higher than the chi-square of the full model (model 1), it can be concluded that there exists an overlap between the descriptive power of the two groups of variables (Bakker et al. 2005a).”