Expanding risk consideration in integrated models – The role of downside risk aversion in irrigation decisions

Finger, Robert

doi:10.1016/j.envsoft.2013.02.001

Cited by 27 publications

(24 citation statements)

References 28 publications

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“…We thus assume that cultivation decisions are made based on good agricultural practice with no incentives to produce at lower cost, as there is no protection against low-yield outcomes. 19 In Heimfarth et al (2012), different regression approaches have been considered, that is, linear and quadratic trend models, as well as OLS and robust regression approaches (Finger 2010). ) values in the strike-level section reflect cases of poor quantile regression results with respect to the estimates of intercept and slope coefficient.…”

Section: Resultsmentioning

confidence: 99%

“…11 Compared to weather station data, the gridded 7 See Di Falco and Chavas (2006) and Finger (2013 for a motivation of this functional form. 8 As the power utility function implies decreasing absolute risk aversion (DARA) preferences, we tested sensitivity toward changes in the initial wealth parameter and found no qualitative differences in our results.…”

Section: Data a Weather Datamentioning

confidence: 99%

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Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Dalhaus

Finger

2016

Weather, Climate, and Society

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Adverse weather events occurring at sensitive stages of plant growth can cause substantial yield losses in crop production. Agricultural insurance schemes can help farmers to protect their income against downside risks. While traditional indemnity-based insurance schemes need governmental support to overcome market failure caused by asymmetric information problems, weather index–based insurance (WII) products represent a promising alternative. In WII the payout depends on a weather index serving as a proxy for yield losses. However, the nonperfect correlation of yield losses and the underlying index, the so-called basis risk, constitutes a key challenge for these products. This study aims to contribute to the reduction of basis risk and thus to the addition of risk-reducing properties of WII. More specifically, the study tests whether grid data for precipitation (vs weather station data) and phenological observations (vs fixed time windows for index determination) that are provided by public institutions can reduce spatial and temporal basis risk and thus improve the performance of WII. An empirical example of wheat production in Germany is used. No differences were found between using gridded and weather station precipitation, whereas the use of phenological observations significantly increases expected utility. However, even if grid data do not yet reduce basis risk, they enable overcoming several disadvantages of using station data and are thus useful for WII applications. Based on the study’s findings and the availability of these data in other countries, a massive potential for improving WII can be concluded.

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Section: Resultsmentioning

confidence: 99%

Section: Data a Weather Datamentioning

confidence: 99%

Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Dalhaus

Finger

2016

Weather, Climate, and Society

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“…To investigate the role of risk aversion on optimal nitrogen use decision, we follow Finger (2013) and investigate optimal input use for 4 different scenarios. With being either 0, 1, 2 or 3 taken to represent a gradient from zero to moderate (downside) risk aversion these are: r2 = 0, 1 , 2 and 3 and r3 = 0, − …”

Section: The Economic Modelmentioning

confidence: 99%

“…Calanca 2007), consideration of downside risks is even more important in the context of climate change impact assessments. So far, however, the problem of integrating downside risk aversion in bio-economic models has been tackled only in a few studies Finger 2013;Finger 2013, Finger andCalanca, 2011). The goal of our study was to integrate downside risks in a bio-economic model and examine the implications of risk aversion for optimal nitrogen use in grassland production under current and future climatic conditions.…”

Section: Introductionmentioning

confidence: 99%

Implications of risk attitude and climate change for optimal grassland management: a case study for Switzerland

2014

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We present a bio-economic model by combining a process-based grassland simulation model with an economic decision model that accounts for income risks and yield quality. The model is used to examine optimal nitrogen (N) application rates in a grass–clover system in Switzerland under current and future climatic conditions. Results for present-day climatic conditions suggest that an increase in N inputs has positive effects on yields but also leads to higher yield variability, yield distributions more skewed to the left and therefore higher downside risks. As a result, accounting for farmers’ risk aversion in solving the optimisation problem leads to lower optimal N inputs. Simulations with a climate change scenario that predicts higher temperatures throughout the year and lower rainfall amounts during the growing season indicate higher yields, increasing yield variability, and changes in yield quality. By allowing herbage prices to vary as a function of yield quality, we find overall lower optimal N inputs and more marked effects of risk aversion on optimal N levels under climate change than under present conditions. However, disregarding yield quality in solving the optimisation problem gives higher optimal N inputs under future conditions.

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“…Finger (2013) recently applied the Antle (1987) model to estimate the optimal N fertilizer rate and irrigation rate for corn production while considering both the variance and skewness of net returns and found that downside risk reduced the optimal N fertilizer rate and increased the optimal irrigation rate. Applying this approach to determine optimal N rates for continuous corn, corn grown after cotton, and corn grown after soybean would be a unique application of the Antle (1987) model that augments traditional risk analyses with useful information about downside risk.…”

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confidence: 99%

Risk Effects on Optimal Nitrogen Rates for Corn Rotations in Tennessee

et al. 2015

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e objectives of this study were to determine the e ects of risk exposure on optimal N fertilizer rates for continuous corn (Zea mays L.), corn grown a er cotton (Gossypium ssp.), and corn grown a er soybean [Glycine max (L.) Merr.] and identify the optimal corn rotation for risk-averse corn producers. Data were collected from a 7-yr, corn-rotation, N-fertilizer experiment in Tennessee. Partial budgets were used to calculate net returns to N for corn grown a er corn, corn grown a er cotton, and corn grown a er soybean. e exible moment method was used for risk analysis, a unique application of this method that provides producers with information concerning traditional risk e ects on decisions about crop rotations and N rates augmented by the e ects of downside risk. Results from this study show rotating corn with cotton and soybean increased yields and net returns relative to continuous corn, but pro t-maximizing N rates for corn were not greatly impacted by the crop planted in the previous year. Optimal N rates decreased for producers with more risk aversion, indicating that N fertilizer is a risk-increasing input. Downside risk was found for continuous corn and corn grown a er soybean, increasing the risk premiums for these rotations above the risk premium for corn grown a er cotton. Slightly to moderately risk-averse corn producers would prefer growing corn a er soybean, but highly risk-averse producers would prefer rotating corn with cotton. Results provide corn producers with enhanced information about the e ects of risk exposure on choices among corn rotations and N fertilizer rates.

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Expanding risk consideration in integrated models – The role of downside risk aversion in irrigation decisions

Cited by 27 publications

References 28 publications

Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Implications of risk attitude and climate change for optimal grassland management: a case study for Switzerland

Risk Effects on Optimal Nitrogen Rates for Corn Rotations in Tennessee

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