The Rainfall Index Annual Forage Pilot Program as a Risk Management Tool for Cool-Season Forage

Maples, Joshua G.; Brorsen, B. Wade; Biermacher, Jon T.

doi:10.1017/aae.2016.3

Cited by 12 publications

(19 citation statements)

References 13 publications

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“…Our conceptual model modifies Maples, Brorsen, and Biermacher (2016), by allowing monthly insurance interval selection in a perennial forage system. We assume producers are risk averse, expected-utility maximizers and can choose to purchase insurance.…”

Section: Conceptual Modelmentioning

confidence: 99%

Risk implications from the selection of rainfall index insurance intervals

Westerhold¹,

Walters²,

Brooks³

et al. 2018

AFR

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Purpose The purpose of this paper is to empirically examine the financial outcomes from forage production and RI-PRF insurance interval for two locations in Nebraska. Both locations provide historical forage production and precipitation data, allowing the authors to examine the relation between RI-PRF net income and forage production. Design/methodology/approach The authors focus on evaluating the producer net income and risk (measured as variance of net income) by examining the relation between farm precipitation and production and comparing multiple insurance intervals to no insurance. Each insurance interval will likely have a different relation (basis risk) between observed production and return from insurance and, therefore, a different impact on the variance of net incomes. The impact on variance of net incomes identifies the risk-reducing aspects of RI-PRF insurance intervals. The authors then rank each scenario into four mutually exclusive zones that describe the risk-reducing effectiveness and whether the subsidy is working correctly. Findings The authors found both risk increasing and decreasing insurance intervals exist at both locations. One insurance scenario (low in BBR) provided the highest net income while increasing risk, suggesting a profit maximizing opportunity. RI-PRF reduces net income risk with intervals insuring during high expected precipitation (growing season); while net income risk increases with intervals insuring low expected precipitation (non-growing season, winter months). The farmer would want to insure during the high expected precipitation months, which coincides with the growing season, since RI-PRF lowers the net income risk. For the government, removing net income risk increasing intervals improves the allocation of government resources. Originality/value In this paper, the authors modeled the relation between RI-PRF interval selection using the historical forage production data at two locations in Nebraska. The use of historical forage production data allowed the authors to precisely identify the risk-reducing effectiveness of RI-PRF interval selection.

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Section: Conceptual Modelmentioning

confidence: 99%

Risk implications from the selection of rainfall index insurance intervals

Westerhold¹,

Walters²,

Brooks³

et al. 2018

AFR

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“…It is an expected result as pasture cultivation is quite different from the development of arable crops, where normal distributions in the NDVI values are more common. This high heterogeneity in time and space of NDVI estimated on pasture has been pointed out in several works (Martin-Sotoca et al, 2018). At the same time, the more different the observed NDVI frequency is from a normal distribution, the less representative the average is, and so the median becomes a more representative value.…”

Section: χ 2 Testmentioning

confidence: 86%

“…Two examples of SIBIs are presented in two different countries: the USA and Spain. In particular, in the USA there are several insurance programs for pasture, rangeland and foraging, which use various indexing systems (rainfall and vegetation indices) and are promoted by the Unites States Department of Agriculture (USDA) (Maples et al, 2016;USDA, 2018). NDVI is the index chosen in the vegetation index program, and it is obtained from the AVHRR (Advanced Very High Resolution Radiometer) sensor aboard NOAA satellites.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds

Martín-Sotoca

Saá-Requejo²,

Moratiel³

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Vegetation indices based on satellite images, such as the normalized difference vegetation index (NDVI), have been used in countries like the USA, Canada and Spain for damaged pasture and forage insurance over the last few years. This type of agricultural insurance is called satellite-index-based insurance (SIBI). In SIBI, the occurrence of damage is defined as normal distributions. In this work a pasture area at the north of the Community of Madrid (Spain) has been delimited by means of Moderate Resolution Imaging Spectroradiometer (MODIS) images. A statistical analysis of NDVI histograms was applied to seek for alternative distributions using the maximum likelihood method and χ2 test. The results show that the normal distribution is not the optimal representation and the generalized extreme value (GEV) distribution presents a better fit through the year based on a quality estimator. A comparison between normal and GEV is shown with respect to the probability under a NDVI threshold value throughout the year. This suggests that an a priori distribution should not be selected and a percentile methodology should be used to define a NDVI damage threshold rather than the average and standard deviation, typically of normal distributions. Highlights. The GEV distribution provides better fit to the NDVI historical observations than the normal one. Differences between normal and GEV distributions are higher during spring and autumn, which are transition periods in the precipitation regimen. NDVI damage threshold shows evident differences using normal and GEV distributions both covering the same probability (24.20 %). NDVI damage threshold values based on percentile calculation are proposed as an improvement in the index-based insurance in damaged pasture.

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“…The RIAFP utilizes rainfall indices from weather data collected by NOAA. Maples et al (2016) found that RIAFP was successful in transferring income to participating forage producers, but they did not find a correlation between rainfall and forage yield, so RIAFP was risk increasing rather than risk reducing. Is the premise that forage yield is correlated with monthly precipitation a false assumption?…”

Section: Introductionmentioning

confidence: 94%

“…Is the premise that forage yield is correlated with monthly precipitation a false assumption? Before reaching such a conclusion, however, it is important to note that the study conducted by Maples et al (2016) only studied one site on sandy soil with changing species over time. Inclusion of multiple sites with different soil types and consistent species could provide information on the robustness and generality of their findings.…”

Section: Introductionmentioning

confidence: 99%

Design of the rainfall index annual forage program

ShalekBriski

Brorsen

Rogers

et al. 2020

AFR

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PurposeThe authors determine the effectiveness of the Rainfall Index Annual Forage Program (RIAFP) in offsetting yield risk of winter annual forage growers. The authors also evaluate the effectiveness in reducing risk of potential alternative weather indices.Design/methodology/approachThe RIAFP is designed to compensate forage producers when yield losses occur. Prior research found weak correlation between the rainfall index and actual winter annual forage yields. The authors use long-term small-plot variety trials of rye, ryegrass, wheat, triticale and oats with rainfall recorded on site and measure the correlation of the index with actual rainfall and actual yields. The alternative indices include frequency of precipitation events and of days with temperature below freezing.FindingsThe correlation between actual rainfall and the current RMA index was strongly positive as in previous research. Correlations between forage yields and monthly intervals of the current RMA index were mostly statistically insignificant, and many had an unexpected sign. All indices had some correlations that were inconsistent across time intervals and forage variety. The inconsistent signs suggest a nonlinear relationship with weather and forage yield, indicating that rainfall can be too much or too little. The number of days below freezing has the most potential of the three measures examined.Practical implicationsProducers should view the winter forage RIAFP as a risk-increasing income-transfer farm program. A product to reduce the risk for forage producers may need to use a crop growth simulation model or another approach that can capture the nonlinearity.Originality/valueConsiderably more data were considered than in past research. Past research did not consider alternative weather indices. The program should be continued if its goal is to serve as disguised income transfer, but it should be discontinued if its goal is to reduce risk.

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The Rainfall Index Annual Forage Pilot Program as a Risk Management Tool for Cool-Season Forage

Cited by 12 publications

References 13 publications

Risk implications from the selection of rainfall index insurance intervals

Risk implications from the selection of rainfall index insurance intervals

Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds

Design of the rainfall index annual forage program

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