Evaluating Pest Management Strategies: A Robust Method and its Application to Strawberry Disease Management

Ariel Soto-Caro; Feng Wu; Natalia Peres; Zhengfei Guan

REVIEW 4 major objections 6 minor 15 references

Evaluating Pest Management Strategies: A Robust Method and its Application to Strawberry Disease Management

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The paper claims that a simulation-based certainty-equivalent method, built on quantile regression and disease-pressure data, can rank fungicide treatments more reliably than standard field-replicate rankings.

desk verdict A plausible SERF extension for disease-pressure scenarios, but the ranking engine rests on pseudo-replication and the scenario CEs are essentially refits of the same small dataset. read the letter →

arxiv 1908.01808 v2 pith:JTF5C3IO submitted 2019-08-05 econ.GN q-fin.EC

Ariel Soto-Caro , Feng Wu , Zhengfei Guan , Natalia Peres This is my paper · ORCID

classification econ.GNq-fin.EC

keywords botrytisstrawberrycertaintyequivalentquantileregressionsimulationriskefficiencyfungicidediseasepressure

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that the standard way of ranking risky pest-management options—computing certainty equivalents from a few field replicates—gives unstable, context-free answers because it has too few observations and ignores disease pressure. To fix this, the authors estimate a quantile regression of strawberry yield on lagged yield, a Botrytis infection index, treatment indicators, and a time trend, then simulate 100 yield draws for each of nine scenarios spanning low, medium, and high disease incidence and low, medium, and high yield quantiles. Feeding these simulated yields into a power-utility certainty equivalent produces treatment rankings that change with disease pressure and yield level: Serenade is risk-efficient in low-yield quantiles, while the untreated control beats Fracture and Milstop. If the method is right, pest-management recommendations can be made scenario-specific and statistically grounded even when field trials have few replicates.

What carries the argument

The load-bearing object is the quantile-regression simulation model of yield, equation (3), written here as $Yield_t = \beta_0 + \beta_1 Yield_{t-1} + \beta_2 BII_t + \beta_3 t + \sum_i \gamma_i D_i + t \sum_i \delta_i D_i$. The regression is estimated at nine quantiles; the coefficient on the Botrytis Incidence Index carries the disease-pressure effect, while the treatment dummies and their time interactions carry the treatment effects. The estimated equations are then used to generate 100 simulated yield series under low (10–30%), medium (40–60%), and high (70–90%) Botrytis incidence and at low (0.2), middle (0.5), and high (0.8) yield quantiles. These simulated profits enter the power-utility certainty equivalent formula, so the certainty-equivalent ranking becomes a function of an explicit disease scenario, a yield level, and the assumed degree of risk aversion.

What would settle it

Recompute the quantile regression with standard errors clustered by plot rather than by harvest record, then rerun the 100 simulations per scenario; if the certainty-equivalent bands for different treatments overlap within a scenario, the claimed ranking differences are not statistically separable.

Watch

Extended reading notes

Core claim

Read sympathetically, the paper's discovery is that the certainty-equivalent ranking of fungicide treatments is not a single answer but a family of answers indexed by disease pressure and yield level. By fitting a quantile regression of strawberry yield on lagged yield, the Botrytis incidence index, treatment indicators, and a time trend, and then simulating 100 yield realizations in each of nine scenarios (three disease-incidence ranges times three yield quantiles), the authors obtain certainty-equivalent rankings that would be invisible in the eight profit observations per treatment available from two field seasons. The simulated rankings show Serenade consistently risk-efficient in the lower part of the yield distribution and, at higher yield quantiles, only for strongly risk-averse growers; the untreated control dominates Fracture and Milstop at all risk-aversion levels. The authors conclude that this procedure is a more reliable tool for identifying risk-efficient treatments because it overcomes limited replication and incorporates disease pressure.

Load-bearing premise

The method's statistical power comes from treating each individual harvest from a plot as an independent data point, even though those harvests are repeated measurements from the same experimental unit; if plot-level correlation is substantial, the simulated rankings could be less precise than they appear.

Editorial extensions

If this is right

The standard certainty-equivalent ranking computed from raw field data is not stable across seasons, while the simulated procedure replaces it with scenario-specific rankings that remain consistent with the raw-data results in most cases.
Serenade is risk-efficient at lower yield quantiles across all disease scenarios, while at higher yield quantiles it is selected only by farmers with stronger risk aversion.
The untreated control outperforms Fracture and Milstop at every risk-aversion level considered in the simulated rankings.
The sensitivity analysis with lower and higher strawberry prices preserves the same qualitative conclusions, suggesting the scenario rankings are not driven by the particular price assumption.
Because the machinery only requires an exogenous driver of yield and a treatment indicator, the same simulation-plus-certainty-equivalent workflow could be applied to other crops and other stress factors.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An extension the paper leaves implicit is disease forecasting as a decision tool: a grower who knows whether the coming season will bring low, medium, or high Botrytis pressure can read the corresponding certainty-equivalent panel and choose the treatment matched to that forecast.
Because each harvest within a plot is treated as an independent observation, the precision of the simulated rankings is plausibly an upper bound; a plot-level cluster bootstrap would give a direct test of whether the scenario rankings survive when the repeated harvests are treated as one experimental unit.
The nine-scenario grid could be replaced by a continuous risk map built from the quantile-regression coefficients, allowing recommendations for any forecasted Botrytis incidence value rather than three coarse bands.
The same logic applies beyond pesticides: any input whose payoff depends on an exogenous stress index, such as irrigation under drought or variety choice under disease risk, could be ranked with this procedure.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know this before reading: the paper's selling point—that simulations fix the low statistical power of SERF—does not hold as presented. The quantile regression in Eq. (3) uses 768 harvest-level records but only 32 plot-season clusters, and it treats repeated harvests from the same plot as independent, even including a lagged dependent variable that makes the dependence explicit. The simulated CE rankings in Figure 4 inherit that overstated precision, so the 'more reliable tool' claim is not supported by the analysis.

What is new and worth credit: the paper adds an exogenous Botrytis Incidence Index to a quantile regression and feeds the simulated yield distributions into certainty-equivalent rankings under nine disease/yield scenarios. That specific combination is a real, modest extension of the standard SERF-plus-simulation practice cited from Hardaker et al. (2004) and Richardson and Outlaw (2008). The writing is clear, the strawberry application is realistic, and the price-sensitivity checks in the appendix are a good instinct.

Where it is soft, in proportion: first, the pseudo-replication issue is load-bearing, not cosmetic. Eight profit observations per treatment is a real constraint, but generating 100 simulated yields from coefficients estimated on those same eight plots does not create independent evidence. Second, the simulation algorithm is underspecified: no mention of how residuals are drawn, whether parameter uncertainty is carried through, or how the 100 replications are aggregated. Third, as you noted, the scenario CEs reduce to fitted values of Eq. (3), so the rankings are deterministic consequences of that model with no uncertainty bounds. Fourth, the Discussion's sentence calling the procedure 'a more reliable tool' overreaches relative to what the statistics support. The central argument is not incoherent; the application is honest and the ideas are clearly explained. It just needs a different statistical backbone: cluster-robust or multilevel inference, a properly specified simulation that propagates estimation uncertainty, and ideally out-of-sample validation.

Who it is for: applied agricultural economists and extension scientists comparing pesticide treatments under disease risk. It deserves a serious referee, not a desk reject, because the scenario-sweep concept is useful and the flaws are fixable. For a journal submission I would require major revision; for a conference paper, it is a legitimate work-in-progress with a clearly named weakness. I would not cite it in its current form, but I would read a revised version.

Referee Report

4 major / 6 minor

Summary. The paper proposes a modification of the standard stochastic efficiency with respect to a function (SERF) method for ranking pest-management treatments under risk and uncertainty. Using harvest-level yield data from strawberry field trials with four treatments and four replications over two seasons, the authors estimate a quantile regression of yield on lagged yield, the Botrytis Incidence Index (BII), a time trend, and treatment dummies and interactions. They then simulate 100 yield draws for three levels of BII (low, medium, high) and three yield quantiles (0.2, 0.5, 0.8), compute certainty equivalents (CE) from the simulated profits under a power utility function, and compare treatment rankings across the resulting nine scenarios. The central claim is that this simulation-based procedure overcomes the limited number of field replicates and, by conditioning on disease pressure, is 'a more reliable tool' for risk-efficiency analysis than the standard SERF approach. The application to Florida strawberry disease management shows that Serenade is often risk-efficient at lower yield quantiles and under higher disease pressure, while the control dominates Fracture and Milstop.

Significance. If the method were statistically valid, it would be a useful practical extension of SERF by allowing risk rankings to be conditioned on exogenous disease pressure and on the location in the yield distribution. The paper is clearly written and addresses a real problem in applied agricultural economics. The quantile-regression approach is a sensible way to summarize treatment differences across the yield distribution. However, the central claim of improved statistical power is not supported by the analysis, and the current formulation makes the scenario rankings a deterministic function of the estimated coefficients. These issues need to be resolved before the contribution can be assessed.

major comments (4)

[Section 3.2, Eq. (3)] The quantile regression in Eq. (3) is estimated on 768 harvest-level observations (48 harvests × 4 replicates × 4 treatments), but the experimental units are the plot-season combinations, of which there are only 32 in total (8 per treatment). The 24 harvests within each plot are serially correlated repeated measurements, and the regression itself includes Yield(t-1), making the dependence explicit. Treating these observations as independent inflates the precision of the coefficients reported in Table 2. The simulated yield distributions and the resulting CE rankings in Figure 4 inherit this overstatement, so the claim that the simulation overcomes the limited number of field replicates is not supported. The analysis should either use cluster-robust standard errors at the plot level or a hierarchical/mixed model that accounts for plot-level correlation, and should acknowledge that the effective sample size for treatment comparisons remains eight per treatment.
[Section 3.2 and Figure 4] The scenario CEs are computed entirely from the quantile regression coefficients estimated on the same field-trial data used for the original CE analysis. By the structure of Eq. (3) and (4), the simulated yields are deterministic functions of the estimated coefficients, and the 100 simulation draws only add noise around the fitted conditional quantile function; they cannot create independent information about treatment effects. The rankings displayed in Figure 4 therefore reduce, by the paper's own equations, to the fitted values of the model. To substantiate the 'more reliable tool' claim, the authors would need to validate the simulated yield distributions against out-of-sample data or against plot-level bootstrap replications, or alternatively reframe the contribution as a conditional scenario analysis that does not claim additional statistical power.
[Section 4 (Discussion)] The concluding statement that the proposed method 'is a more reliable tool to find risk-efficient treatments under different circumstances' is not supported by any formal comparison with the standard SERF approach. The only sensitivity analysis in the Appendix varies strawberry prices and does not address the statistical reliability of the rankings. A concrete test would be to compute the sampling distribution of CE rankings from both methods using plot-level bootstrap resampling and to report coverage or mean-squared-error metrics. Without such evidence, the reliability claim is an assertion rather than a demonstrated property.
[Section 2.3 (Alternative procedure)] The simulation algorithm is not described in sufficient detail for replication. It is unclear whether the 100 yield simulations are draws of uniform quantiles from the estimated conditional quantile function, residual bootstraps, or some other mechanism, and whether uncertainty in the estimated coefficients is propagated. This information is essential for assessing whether the reported CE differences are meaningful and for reproducing the results.

minor comments (6)

[Section 2.1] The trial appears to include four treatments (untreated control plus three fungicides), but the text says 'three fungicide treatments'; please clarify that the control is included as a treatment.
[Equation (1)] The symbol R is used both for the number of replicates (in the summation upper limit) and as the replicate index; please use distinct symbols (e.g., N and r) to avoid confusion.
[Table 2] Several standard errors appear as negative numbers (e.g., -0.03 for Yield(t-1) at Q=0.1 and -39.15, -79.1, etc.), which is likely a formatting artifact; please correct the table.
[Figure 4] The axes and line labels are not described in the text or caption; please add a clear description of what is plotted, including the definition of the lines.
[References] The in-text citation 'Cordoba et al., 2014' does not match the reference list entry 'Cordova, L., Zuniga, A., ...'; please fix the spelling and ensure all citations are consistent.
[Section 2.1] The Botrytis Incidence Index is said to be obtained from AgroClimate, but no reference or construction details are provided; please add a citation or a brief description.

Circularity Check

1 steps flagged · score 6.0 of 10

Scenario CE 'predictions' are deterministic re-expressions of the quantile regression fitted to the same field trial; the claimed power gain from simulation is inherited, not independent.

fitted input called prediction [Section 2.3, Eq. (4) using Eq. (3); Section 3.2, Table 2 and Figure 4]
"We introduced the exogenous BII index into a quantile-regression model to estimate the relationship between weather factors and strawberry yield, which was used to simulate sufﬁcient yield observations under different weather scenarios. Then these simulated yield estimates rather than the actual observations were used to compute CE in order to increase the statistical power. ... Equation (3) is estimated with 768 observations (48 harvests × 4 replicates× 4 treatments); its coefﬁcients are presented in Table 2."

By Eq. (4), the simulated profit is ~π_{n,M} = Σ(P_n·Y_{n,M}(~BII) − TC), and Y_{n,M}(~BII) is generated from Eq. (3) using the coefficients in Table 2. Those coefficients come from the same 768 observations, which are themselves 48 harvests × 4 replicates × 4 treatments, i.e., repeated harvests from only 32 plot-season units (8 per treatment). The CE rankings in Figure 4 are therefore deterministic functions of the fitted coefficients; the 100 simulations per scenario add no independent information and cannot of themselves 'increase the statistical power.' The scenario results are re-expressions of the fitted model, not predictions from new data.

full rationale

The paper's central claim is that the simulation-based CE procedure is 'a more reliable tool' than standard SERF because it overcomes limited field replication and incorporates disease pressure. The scenario-specific CEs in Figure 4, however, are computed by substituting simulated yields from Eq. (3) into Eq. (4), where the coefficients of Eq. (3) are estimated on the same field trial data. Thus the simulated CE rankings are not independent predictions; they are deterministic transformations of the fitted quantile-regression coefficients. The purported gain in statistical power comes from treating the 768 harvest-level records as independent even though the 24 harvests within each plot are repeated measurements of the same experimental unit, so the added 'observations' are generated from the model's own assumptions rather than from new information. This is a fitted-input-called-prediction pattern: the predicted scenario rankings reduce, by the paper's own equations, to the fitted model. The paper does not claim a formal theorem or rely on a self-citation chain, and it does present ordinary observed-data CE as a comparison, which provides some independent grounding, but the load-bearing scenario results are not independent of the estimation input. Score 6 reflects partial circularity: the central methodological claim is supported by simulation outputs that are constructed from the fitted model rather than validated against external or out-of-sample evidence.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a small number of hand-chosen scenario parameters (RAC range, yield quantiles, BII levels, simulation count), an unreported initial wealth term, and several domain assumptions about the BII index, the cost data, and the quantile regression specification. The estimated quantile regression coefficients are the core fitted inputs that determine all simulated CE values.

free parameters (5)

RAC range = 0.5 to 4
The certainty equivalent and risk premium depend on the relative risk aversion coefficient; the paper sweeps this range (Anderson and Dillon, 1992) rather than estimating it, and the treatment ranking can change within the range.
Initial wealth w0
Eq. (1) includes w0 in the power utility CE calculation, but the paper never states its value; the computed CEs depend on this omitted input.
Yield quantiles = 0.2, 0.5, 0.8
The simulation scenarios use low, average, and high yield quantiles chosen by the authors; the CE curves are conditional on these quantile levels.
BII scenario levels = 10-30%, 40-60%, 70-90%
Low, medium, and high disease incidence ranges are defined by the authors to build the nine scenarios; the resulting rankings are conditional on these arbitrary cut points.
Number of simulations = 100
The paper draws 100 yield simulations per scenario without a power analysis or convergence check; the precision of simulated CEs depends on this choice.

assumptions (5)

domain assumption The power utility function is the correct preference representation for the decision-maker and the CE formula from Hardaker et al. (2004) applies.
Used in Eq. (1); if farmers' true utility is not power utility, CE rankings do not reflect risk preferences. Standard in the SERF literature but still a behavioral assumption.
domain assumption The Botrytis Incidence Index (BII) adequately measures disease pressure and its effect on yield.
The paper relies on BII as the exogenous driver of yield in Eq. (3); it notes a high correlation between BII and losses but provides no quantitative evidence, and the index comes from a separate tool without validation here.
ad hoc to paper The linear quantile regression with lagged yield, BII, and time trend is correctly specified and the 768 harvest-level observations can be treated as independent.
Eq. (3) is the engine of the simulation; the independence assumption across repeated harvests from the same plot is not justified, so the model's standard errors and the resulting simulated distributions may be invalid.
ad hoc to paper The simulated yield draws from the estimated quantile regression reproduce the true yield distribution under each BII scenario.
Section 2.3 states 'we generated 100 yield simulations' without specifying the error sampling method or validating the simulated distribution against observed yields; if the draws are inaccurate, the entire CE scenario analysis is invalid.
domain assumption Costs from Guan et al. (2017) updated by PPI and USDA-AMS prices are representative of the trial seasons.
Profit in Eq. (2) depends on these external cost and price series; errors in those inputs shift all CEs and could change rankings.

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Cite this review

Pith. "Pith review of Evaluating Pest Management Strategies: A Robust Method and its Application to Strawberry Disease Management." pith.science (2026). https://pith.science/paper/JTF5C3IO

@misc{pith2026190801808,
  author       = {Pith},
  title        = {Pith review of: Evaluating Pest Management Strategies: A Robust Method and its Application to Strawberry Disease Management},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JTF5C3IO}},
  note         = {Machine review of arXiv:1908.01808}
}

read the original abstract

Farmers use pesticides to reduce yield losses. The efficacies of pesticide treatments are often evaluated by analyzing the average treatment effects and risks. The stochastic efficiency with respect to a function is often employed in such evaluations through ranking the certainty equivalents of each treatment. The main challenge of using this method is gathering an adequate number of observations to produce results with statistical power. However, in many cases, only a limited number of trials are replicated in field experiments, leaving an inadequate number of observations. In addition, this method focuses only on the farmer's profit without incorporating the impact of disease pressure on yield and profit. The objective of our study is to propose a methodology to address the issue of an insufficient number of observations using simulations and take into account the effect of disease pressure on yield through a quantile regression model. We apply this method to the case of strawberry disease management in Florida.

Figures

Figures reproduced from arXiv: 1908.01808 by the authors.

**Figure 2.** Empirical distribution of yield for each treatment, seasons 2014-15 and [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** Certainty Equivalents using current data. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Simulated CE (color lines are the same as previous figures). [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

**Figure 5.** Figure 5: Simulation with price 11.5 imposed [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]

**Figure 6.** Figure 6: Simulation with price 30 imposed. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

[1]

\ Dillon, J L

Anderson1992 APACrefauthors Anderson, J R. \ Dillon, J L. APACrefauthors \ 1992 . Risk analysis in dryland farming systems Risk analysis in dryland farming systems . Rome Food and Agriculture Organization of the United Nations, Series No 2

work page 1992
[2]

\ Sutton, J

Braun1987 APACrefauthors Braun, P. \ Sutton, J. APACrefauthors \ 1987 3 . Inoculum sources of Botrytis cinerea in fruit rot of strawberries in Ontario Inoculum sources of Botrytis cinerea in fruit rot of strawberries in Ontario . Canadian Journal of Plant Pathology 9 1 1--5 . APACrefDOI doi:10.1080/07060668709501903 APACrefDOI

work page doi:10.1080/07060668709501903 1987
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Chavas2015 APACrefauthors Chavas, J P. \ Shi, G. APACrefauthors \ 2015 . An Economic Analysis of Risk, Management, and Agricultural Technology An Economic Analysis of Risk, Management, and Agricultural Technology . Journal of Agricultural and Resource Economics 40 1 63--79

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Cordova2014 APACrefauthors Cordova, L. , Zuniga, A. , Mertely, J. \ Peres, N. APACrefauthors \ 2014 . Evaluation of products for the control of Botrytis fruit rot in annual strawberry, 2013-14. Evaluation of products for the control of Botrytis fruit rot in annual strawberry, 2013-14. Plant Disease Management Report 8 1 1--2

work page 2014
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Frisvold2019 APACrefauthors Frisvold, G B. APACrefauthors \ 2019 1 . How low can you go? Estimating impacts of reduced pesticide use How low can you go? Estimating impacts of reduced pesticide use . Pest Management Science 75 5 . APACrefDOI doi:10.1002/ps.5249 APACrefDOI

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Guan2017 APACrefauthors Guan, Z. , Wu, F. \ Whidden, A. APACrefauthors \ 2017 . Florida Strawberry Production Costs and Trends - Publication \# FE1013 Florida Strawberry Production Costs and Trends - Publication \# FE1013 \ . Food and Resource Economics Department, UF/IFAS Extension . APACrefURL http://edis.ifas.ufl.edu/fe1013 APACrefURL

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

\ Dillon, J L

Anderson1992 APACrefauthors Anderson, J R. \ Dillon, J L. APACrefauthors \ 1992 . Risk analysis in dryland farming systems Risk analysis in dryland farming systems . Rome Food and Agriculture Organization of the United Nations, Series No 2

work page 1992

[2] [2]

\ Sutton, J

Braun1987 APACrefauthors Braun, P. \ Sutton, J. APACrefauthors \ 1987 3 . Inoculum sources of Botrytis cinerea in fruit rot of strawberries in Ontario Inoculum sources of Botrytis cinerea in fruit rot of strawberries in Ontario . Canadian Journal of Plant Pathology 9 1 1--5 . APACrefDOI doi:10.1080/07060668709501903 APACrefDOI

work page doi:10.1080/07060668709501903 1987

[3] [3]

\ Shi, G

Chavas2015 APACrefauthors Chavas, J P. \ Shi, G. APACrefauthors \ 2015 . An Economic Analysis of Risk, Management, and Agricultural Technology An Economic Analysis of Risk, Management, and Agricultural Technology . Journal of Agricultural and Resource Economics 40 1 63--79

work page 2015

[4] [4]

, Zuniga, A

Cordova2014 APACrefauthors Cordova, L. , Zuniga, A. , Mertely, J. \ Peres, N. APACrefauthors \ 2014 . Evaluation of products for the control of Botrytis fruit rot in annual strawberry, 2013-14. Evaluation of products for the control of Botrytis fruit rot in annual strawberry, 2013-14. Plant Disease Management Report 8 1 1--2

work page 2014

[5] [5]

APACrefauthors \ 2019 1

Frisvold2019 APACrefauthors Frisvold, G B. APACrefauthors \ 2019 1 . How low can you go? Estimating impacts of reduced pesticide use How low can you go? Estimating impacts of reduced pesticide use . Pest Management Science 75 5 . APACrefDOI doi:10.1002/ps.5249 APACrefDOI

work page doi:10.1002/ps.5249 2019

[6] [6]

, Tremblay, D M

Gobeil-Richard2016 APACrefauthors Gobeil-Richard, M. , Tremblay, D M. , Beaulieu, C. , Van der Heyden , H. \ Carisse, O. APACrefauthors \ 2016 . A pyrosequencing-based method to quantify genetic substitutions associated with resistance to succinate dehydrogenase inhibitor fungicides in Botrytis spp. populations A pyrosequencing-based method to quantify ge...

work page 2016

[7] [7]

Guan2017 APACrefauthors Guan, Z. , Wu, F. \ Whidden, A. APACrefauthors \ 2017 . Florida Strawberry Production Costs and Trends - Publication \# FE1013 Florida Strawberry Production Costs and Trends - Publication \# FE1013 \ . Food and Resource Economics Department, UF/IFAS Extension . APACrefURL http://edis.ifas.ufl.edu/fe1013 APACrefURL

work page 2017

[8] [8]

, Richardson, J W

Hardaker2004 APACrefauthors Hardaker, J B. , Richardson, J W. , Lien, G. \ Schumann, K D. APACrefauthors \ 2004 6 . Stochastic efficiency analysis with risk aversion bounds: a simplified approach Stochastic efficiency analysis with risk aversion bounds: a simplified approach . The Australian Journal of Agricultural and Resource Economics 48 2 253--270 . A...

work page arXiv 2004