Quantification and comparison of wheat yield variation across space and time

Florin, M.J.; McBratney, Alex B.; Whelan, Brett

doi:10.1016/j.eja.2008.10.003

Cited by 28 publications

(16 citation statements)

References 19 publications

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“…where x i and x i+h are two data separated by the temporal lag, h. Semivariance has frequently been used to describe spatial variability [Dent and Grimm, 1999;Ewers and Pendall, 2008;Meisel and Turner, 1998] but is also appropriate for temporal data [Carmona-Moreno et al, 2005;Florin et al, 2009].…”

Section: Discussionmentioning

confidence: 99%

“…To identify GPP temporal variability within and between growing seasons we computed the semivariance between daily GPP during each growing season. Semivariance ( γ h ) describes the variability between data separated by a scale lag (h) and is calculated as: where x i and x i+h are two data separated by the temporal lag, h. Semivariance has frequently been used to describe spatial variability [ Dent and Grimm , 1999; Ewers and Pendall , 2008; Meisel and Turner , 1998] but is also appropriate for temporal data [ Carmona‐Moreno et al , 2005; Florin et al , 2009].…”

Section: Methodsmentioning

confidence: 99%

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Gross primary production variability associated with meteorology, physiology, leaf area, and water supply in contrasting woodland and grassland semiarid riparian ecosystems

Jenerette¹,

Scott²,

Barron‐Gafford³

et al. 2009

J. Geophys. Res.

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[1] Understanding ecosystem-atmosphere carbon exchanges in dryland environments has been more challenging than in mesic environments, likely due to more pronounced nonlinear responses of ecosystem processes to environmental variation. To better understand diurnal to interannual variation in gross primary productivity (GPP) variability, we coupled continuous eddy-covariance derived whole ecosystem gas exchange measurements with an ecophysiologic model based on fundamental principles of diffusion, mass balance, reaction kinetics, and biochemical regulation of photosynthesis. We evaluated the coupled data-model system to describe and understand the dynamics of 3 years of growing season GPP from a riparian grassland and woodland in southern Arizona. The data-model fusion procedure skillfully reproduced the majority of daily variation GPP throughout three growing seasons. While meteorology was similar between sites, the woodland site had consistently higher GPP rates and lower variability at daily and interannual timescales relative to the grassland site. We examined the causes of this variation using a new state factor model analysis that partitioned GPP variation into four factors: meteorology, physiology, leaf area, and water supply. The largest proportion of GPP variation was associated with physiological differences. The woodland showed a greater sensitivity than the grassland to water supply, while the grassland showed a greater sensitivity to leaf area. These differences are consistent with hypotheses of woody species using resistance mechanisms, stomatal regulation, and grassland species using resilience mechanisms, leaf area regulation, in avoiding water stress and have implications for future GPP sensitivity to climate variability following wood-grass transitions.Citation: Jenerette, G. D., R. L. Scott, G. A. Barron-Gafford, and T. E. Huxman (2009), Gross primary production variability associated with meteorology, physiology, leaf area, and water supply in contrasting woodland and grassland semiarid riparian ecosystems,

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Gross primary production variability associated with meteorology, physiology, leaf area, and water supply in contrasting woodland and grassland semiarid riparian ecosystems

Jenerette¹,

Scott²,

Barron‐Gafford³

et al. 2009

J. Geophys. Res.

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“…Agricultural yields vary in response to management choices such as crop genotype, fallowing, crop rotation, break crops, sowing date, herbicide use, fertilization rates, and residue management (Anderson et al 2005, Ransom et al 2007, Florin et al 2009. Yields also vary spatially following heterogeneity in the environmental drivers of soil and climate (Luo et al 2003, 2005b, Wang et al 2009, Bryan et al 2010.…”

Section: Introductionmentioning

confidence: 99%

Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps

Bryan

King

Zhao

2014

Environ. Res. Lett.

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In the future, agriculture will need to produce more, from less land, more sustainably. But currently, in many places, actual crop yields are below those attainable. We quantified the ability for agricultural management to increase wheat yields across 179 Mha of potentially arable land in Australia. Using the Agricultural Production Systems Simulator (APSIM), we simulated the impact on wheat yield of 225 fertilization and residue management scenarios at a high spatial, temporal, and agronomic resolution from 1900 to 2010. The influence of management and environmental variables on wheat yield was then assessed using Spearman's non-parametric correlation test with bootstrapping. While residue management showed little correlation, fertilization strongly increased wheat yield up to around 100 kg N ha −1 yr −1 . However, this effect was highly dependent on the key environment variables of rainfall, temperature, and soil water holding capacity. The influence of fertilization on yield was stronger in cooler, wetter climates, and in soils with greater water holding capacity. We conclude that the effectiveness of management intensification to increase wheat yield is highly dependent upon local climate and soil conditions. We provide context-specific information on the yield benefits of fertilization to support adaptive agronomic decision-making and contribute to the closure of yield gaps. We also suggest that future assessments consider the economic and environmental sustainability of management intensification for closing yield gaps.

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“…First, all crops grown at field scales are subjected to significant soil and topographic diversity, which leads to well-recognized spatial variability in plant growth and crop yield (e.g., 10,11). This variability is typically minimized in plot-scale studies by blocking, contiguous designs, and judicious plot layouts (12,13), as well as by locating experiments on more favorable soils (1).…”

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confidence: 99%

Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems

Kravchenko

Snapp

Robertson

2017

Proc. Natl. Acad. Sci. U.S.A.

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Knowledge of production-system performance is largely based on observations at the experimental plot scale. Although yield gaps between plot-scale and field-scale research are widely acknowledged, their extent and persistence have not been experimentally examined in a systematic manner. At a site in southwest Michigan, we conducted a 6-y experiment to test the accuracy with which plot-scale crop-yield results can inform field-scale conclusions. We compared conventional versus alternative, that is, reduced-input and biologically based-organic, management practices for a cornsoybean-wheat rotation in a randomized complete block-design experiment, using 27 commercial-size agricultural fields. Nearby plot-scale experiments (0.02-ha to 1.0-ha plots) provided a comparison of plot versus field performance. We found that plot-scale yields well matched field-scale yields for conventional management but not for alternative systems. For all three crops, at the plot scale, reduced-input and conventional managements produced similar yields; at the field scale, reduced-input yields were lower than conventional. For soybeans at the plot scale, biological and conventional managements produced similar yields; at the field scale, biological yielded less than conventional. For corn, biological management produced lower yields than conventional in both plot-and field-scale experiments. Wheat yields appeared to be less affected by the experimental scale than corn and soybean. Conventional management was more resilient to field-scale challenges than alternative practices, which were more dependent on timely management interventions; in particular, mechanical weed control. Results underscore the need for much wider adoption of field-scale experimentation when assessing new technologies and production-system performance, especially as related to closing yield gaps in organic farming and in low-resourced systems typical of much of the developing world.field experiments | scaling | corn soybean wheat rotation | weed control | organic agriculture

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Quantification and comparison of wheat yield variation across space and time

Cited by 28 publications

References 19 publications

Gross primary production variability associated with meteorology, physiology, leaf area, and water supply in contrasting woodland and grassland semiarid riparian ecosystems

Gross primary production variability associated with meteorology, physiology, leaf area, and water supply in contrasting woodland and grassland semiarid riparian ecosystems

Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps

Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems

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