Use of the beta distribution for parameterizing variability of soil properties at the regional level for crop yield estimation

Haskett, Jonathan; Pachepsky, Yakov

doi:10.1016/0308-521x(95)93646-u

Cited by 28 publications

(17 citation statements)

References 10 publications

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“…It varies roughly linearly with log conductivity, ranging from about 10 cm for sands up to 100 cm for clays, and can be reasonably well-characterized with a normal distribution. Presumably, the initial saturation deficit, S d D jÂ s Â 0 j can also be represented using a normal distribution (Patgiri and Baruah, 1995), but due to the presence of fixed upper and lower bounds is likely better represented using a beta or uniform distribution (Haskett et al, 1995;Det Norske Veritas, 1997). Multiple field studies of soil moisture distributions have discovered wildly varying distributional characteristics, depending upon characteristics as varied as soil texture, storm duration, Figure 4.…”

Section: Variable Initial Soil Moisture/wetting Front Suctionmentioning

confidence: 98%

Runoff–infiltration partitioning using an upscaled Green–Ampt solution

Craig

Liu

Soulis

2010

Hydrological Processes

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Abstract:A new set of formulae for calculating regionally averaged infiltration rates into heterogeneous soils is presented. The solutions are based upon an upscaled approximation of the explicit Green-Ampt (GA) infiltration solution, and require specification of the spatial distribution of saturated hydraulic conductivity and/or initial soil water deficit in the sub-basin. The resultant areal averaged infiltration formulae, which ignore the impacts of run on or spatial correlation, are easily integrated into existing distributed surface water schemes, and can also be used to calculate saturated soil surface area. The impacts of preferential flow may be investigated through the use of a bimodal conductivity distribution. The solutions are tested against Monte Carlo simulations and assessed for accuracy. Interesting results are obtained regarding the impacts of upscaling on GA infiltration, most notably that the cumulative infiltration is most impacted by low-conductivity soils and that calibration of the standard (point-scale) GA equation to basin-scale hydrographs will lead to an underestimation of average system hydraulic conductivity.

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Section: Variable Initial Soil Moisture/wetting Front Suctionmentioning

confidence: 98%

Runoff–infiltration partitioning using an upscaled Green–Ampt solution

Craig

Liu

Soulis

2010

Hydrological Processes

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“…Dispersal rates in [1] were generated from a beta distribution with a mean value taken from Gonzalez et al (2009) and a user-defined coefficient of variation. The beta distribution, bounded between [0, 1], is often used in modeling dispersal rates (Wiley et al, 1989) and proportions (Haskett et al, 1995). We tested several coefficients of variation ranging between [0, 1].…”

Section: Robustness and The Spatial Correlation Of Risk: Extensionmentioning

confidence: 99%

Biodiversity, productivity, and the spatial insurance hypothesis revisited

Shanafelt

Dieckmann²,

Jonas³

et al. 2015

Journal of Theoretical Biology

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Accelerating rates of biodiversity loss have led ecologists to explore the effects of species richness on ecosystem functioning and the flow of ecosystem services. One explanation of the relationship between biodiversity and ecosystem functioning lies in the spatial insurance hypothesis, which centers on the idea that productivity and stability increase with biodiversity in a temporally varying, spatially heterogeneous environment. However, there has been little work on the impact of dispersal where environmental risks are more or less spatially correlated, or where dispersal rates are variable. In this paper, we extend the original Loreau model to consider stochastic temporal variation in resource availability, which we refer to as “environmental risk,” and heterogeneity in species dispersal rates. We find that asynchronies across communities and species provide community-level stabilizing effects on productivity, despite varying levels of species richness. Although intermediate dispersal rates play a role in mitigating risk, they are less effective in insuring productivity against global (metacommunity-level) than local (individual community-level) risks. These results are particularly interesting given the emergence of global sources of risk such as climate change or the closer integration of world markets. Our results offer deeper insights into the Loreau model and new perspectives on the effectiveness of spatial insurance in the face of environmental risks.

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“…One approach involves disaggregating the region into approximately homogeneous sub-regions in a type of biophysical typology (Hazeu et al, 2010) with associated AgMIP Sentinel Sites for calibrated crop model simulations, and then converting yields to regional production using planted areas in each sub-region (Burke et al, 1991;de Jager et al, 1998;Yu et al, 2010;Ruane et al, in press-b). Another approach uses multivariate sensitivity tests to cast probabilistic distributions of farm-level conditions into an estimate of regional production (Haskett et al, 1995). In a third approach, farm behavior is explicitly taken into account, and crop models are linked to farm economic models to provide farm production estimates, which can subsequently be upscaled through response functions (Pérez Domínguez et al, 2009;Ewert et al, 2009).…”

Section: Aggregation and Scalingmentioning

confidence: 99%

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies

Rosenzweig

Jones

Hatfield

et al. 2013

Agricultural and Forest Meteorology

835

562

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Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.; Basso, B.; Ewert, F.; Wallach, D.; Baigorria, G.; and Winter, J. M., "The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies" (2013 a b s t r a c tThe Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones. Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to [CO 2 ] with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with midcentury climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations' resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.

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Use of the beta distribution for parameterizing variability of soil properties at the regional level for crop yield estimation

Cited by 28 publications

References 10 publications

Runoff–infiltration partitioning using an upscaled Green–Ampt solution

Runoff–infiltration partitioning using an upscaled Green–Ampt solution

Biodiversity, productivity, and the spatial insurance hypothesis revisited

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies

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