Spatial Yield Risk Across Region, Crop and Aggregation Method

Popp, Michael P.; Rudstrom, Margot; Manning, Patrick

doi:10.1111/j.1744-7976.2005.00408.x

Cited by 28 publications

(19 citation statements)

References 9 publications

(17 reference statements)

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“…Thus, yield stability, as defined by variability over time and space, can be measured by (i) average yield and (ii) the CV (i.e., CV%; Berzsenyi et al, 2000; Dobermann et al, 2003). These yield stability metrics can then be aggregated over larger geographic areas (Popp et al, 2005). In conventional agronomic studies, yield across years is measured by location–year data (e.g., county yield for each year within a period of years of observation).…”

Section: Methodsmentioning

confidence: 99%

“…However, an expanding literature by ecologists, geographers, geologists, and climatologists addresses yield variability in association with environmental heterogeneity beyond the plant scale (Day et al, 2003; Hutchings and John, 2004; Si and Farrell, 2004; Waltman et al, 2004; Williams et al, 2008). From this literature, spatially explicit predictive (versus explanatory) modeling (e.g., Legendre et al, 2002; Lobell and Ortiz‐Monasterio, 2006; Miller et al, 2007; Popp et al, 2005; Williams et al, 2008), and concepts of the association of different potential limiting factors with different spatial patterns of yield variability (Lobell and Ortiz‐Monasterio, 2006) have emerged. Applied to regional scales, these approaches offer opportunities for identifying longer‐term spatial patterns of yield variability in association with environmental heterogeneity and thus, a vital first step in developing hypotheses about causes of their formation (Begon et al, 1990).…”

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confidence: 99%

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Patterns of Regional Yield Stability in Association with Regional Environmental Characteristics

et al. 2008

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Regional‐level recurring spatial patterns of yield variability are important for commercial activities, strategic agricultural planning, and public policy, but little is known about the factors contributing to their formation. An important step to improve our understanding is recognizing regional spatial patterns of yield variability in association with regional environmental characteristics. We examined the spatial distribution of county‐level mean yields and CVs of mean yields of four functionally different crops—corn (Zea mays L.), soybean [Glycine max (L.) Merr.], alfalfa (Medicago sativa), and oat (Avena sativa L.)—in Iowa using Moran's Index of spatial autocorrelation. Patterns of association with 12 county‐level climatic, edaphic, and topographic environmental characteristics were examined using partial least squares regression. Two distinct geographic provinces of yield stability were identified: one in the northern two‐thirds of the state characterized by high mean yields and high yield constancy, and one in the southern third of the state characterized by low mean yields and low yield constancy. Among eight partial least squares regression models, which explained 50 to 81% of variation of mean yields and yield CVs, mean organic matter and mean depth to seasonally high water table had greatest relative importance to mean yields of grass crops and legume crops, respectively. Among the CV models, variables describing water availability were of greatest relative importance, with less distinct differences between grass and legume crops. Partial least squares regression is a potentially powerful tool for understanding regional yield variability.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Patterns of Regional Yield Stability in Association with Regional Environmental Characteristics

et al. 2008

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“…Therefore, county‐level data might result in insurance providers paying larger indemnity payments than necessary and farmers purchasing higher coverage than necessary. Methods to control for potential bias arising from aggregation of data have emerged (Popp et al., ; Rudstrom et al., ; Wang and Zhang, ), but complete control of spatial heterogeneities is likely impossible. There is a need for more research on crop yield distribution using field‐level data.…”

Section: Introductionmentioning

confidence: 99%

Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates

Boyer

Brorsen

Tumusiime

2014

Agricultural Economics

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Accurate modeling of skewness is needed to increase the actuarial fairness of crop insurance. We test Day's conjecture that crop yield skewness becomes negative as nitrogen rates increase and determine how well a linear response stochastic plateau (LRSP) production function matches the pattern of observed skewness using four long-term nitrogen experiments. Stillwater wheat is consistent with Day's conjecture, but the skewness for Lahoma and Altus wheat yields as well as Altus cotton yields are not. The LRSP assumes normal random effects and can explain only a small part of observed skewness, so a new LRSP with skew-normal random effects is introduced, which comes closer to explaining the observed skewness and should increase the accuracy of nitrogen rate recommendations. Negative skewness reduced optimal nitrogen rates and positive skewness increased optimal nitrogen rates.JEL classifications: C49, D24, Q19

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“…Popp et al (2005) also reported substantial variation in their yield deviates between farm level yields and yields at the Canadian municipality level.…”

Section: Introductionmentioning

confidence: 90%

Implications of within county yield heterogeneity for modeling crop insurance premiums

Cooper

Zulauf

Langemeier

et al. 2012

Agricultural Finance Review

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Purpose -Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to make full use of the information inherent in each of these data sets. Design/methodology/approach -The paper uses a novel application of statistical tools for using farm and county level yield data to generate farm level yield densities that explicitly incorporate within county yield heterogeneity while accounting for systemic risk and other spatial or intertemporal correlations among farms within the county.Findings -The empirical analysis shows that current approaches used by the Risk Management Agency to individualize premiums for a farm result in substantial mispricing of crop insurance premiums because they do not adequately capture farm yield variability and yield correlations between farms. The new premium setting method is empirically shown to substantially reduce government subsidies for crop insurance premiums. Originality/value -The paper demonstrates how to extract more information from available data when setting crop insurance premiums, which allows the government to more closely tailor premiums to the farm than do current approaches.

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Spatial Yield Risk Across Region, Crop and Aggregation Method

Cited by 28 publications

References 9 publications

Patterns of Regional Yield Stability in Association with Regional Environmental Characteristics

Patterns of Regional Yield Stability in Association with Regional Environmental Characteristics

Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates

Implications of within county yield heterogeneity for modeling crop insurance premiums

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