Economic Value of Weather and Climate Forecasts

Case Studies: Agriculture

Agriculture

Corn | Crop Mix | Livestock | Wheat | Other




Corn



Luo, H., Skees, J.R., and Marchant, M.A. (1994). Weather information and the potential for inter-temporal adverse selection in crop insurance. Review of Agricultural Economics, 16, 441-451.
Mjelde, J.W., and Penson, J.B. (2000). Dynamic aspects of the impact of the use of perfect climate forecasts in the Corn Belt region. Journal of Applied Meteorology, 39, 67-79.
Mjelde, J.W., Thompson, T.N., Hons, F.M., Cothren, J.T., and Coffman, C.G. (1997). Using Southern Oscillation information for determining corn and sorghum profit-maximizing input levels in East-Central Texas. Journal of Production Agriculture, 10, 168-175.


Study Luo, H., Skees, J.R., and Marchant, M.A. (1994). Weather information and the potential for inter-temporal adverse selection in crop insurance. Review of Agricultural Economics, 16, 441-451.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: whether corn producers should buy crop insurance
Dynamics: no
Time Scale: seasonal
Predictand: temperature
Format: probabilistic
Type: realistic
Quality Changes: no
Baselines: climatological
VOI, imperfect: $0.30-$0.86 per dollar of insurance premium (US $)
VOI, perfect: not reported
Risk Treatment: expected value
Comments Forecast value varies depending on how forecasts are translated into effects on corn yields. With no forecast, optimal decision rule is to always purchase insurance. Decision rules are suboptimal in case of imperfect forecasts.


Study Mjelde, J.W., and Penson, J.B. (2000). Dynamic aspects of the impact of the use of perfect climate forecasts in the Corn Belt region. Journal of Applied Meteorology, 39, 67-79.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: fertilizer application (amount & timing), planting date & density, hybrid selection, harvest date
Dynamics: yes
Time Scale: seasonal
Predictand: precipitation, temperature, and radiation
Format: not applicable
Type: not applicable
Quality Changes: no
Baselines: climatological
VOI, imperfect: not reported
VOI, perfect: $1.3-$2.9 billion (US $) over 10-yr horizon
Risk Treatment: expected value
Comments Addresses impacts of use of forecasts beyond agricultural sector. Consumers are winners, producers are losers.


Study Mjelde, J.W., Thompson, T.N., Hons, F.M., Cothren, J.T., and Coffman, C.G. (1997). Using Southern Oscillation information for determining corn and sorghum profit-maximizing input levels in East-Central Texas. Journal of Production Agriculture, 10, 168-175.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: fertilizer application level, planting date, and seeding rate
Dynamics: yes
Time Scale: seasonal
Predictand: precipitation
Format: probabilistic
Type: realistic, derived
Quality Changes: no
Baselines: climatological
VOI, imperfect: $1-$2/acre (US $)
VOI, perfect: not reported
Risk Treatment: expected value
Comments Forecast value is for corn producers (no value for sorghum producers) and varies with crop price. Forecast is based on teleconnections with Southern Oscillation Index.
Related Studies Mjelde, J.W., Hill, H.S.J., and Griffiths, J.F. (1998). A review of current evidence on climate forecasts and their economic effects in agriculture. American Journal of Agricultural Economics, 80, 1089-1095.




Crop Mix



Chen, C.-C., McCarl, B. and Hill, H. (2002). Agricultural value of ENSO information under alternative phase definition. Climatic Change, 54, 305-325.
Letson, D., Podesta, G.P., Messina, C.D., and Ferreyra, R.A. (2005). The uncertain value of perfect ENSO phase forecasts: Stochastic agricultural prices and intra-phase climatic variations. Climatic Change, 69, 163-196.
Messina, C.D., Hansen, J.W., and Hall, A.J. (1999). Land allocation conditioned on ENSO phases in the Pampas of Argentina. Agricultural Systems, 60, 197-212.
Mjelde, J.W., Thompson, T.N., and Nixon, C.J. (1996). Government institutional effects on the value of seasonal climate forecasts. American Journal of Agricultural Economics, 78, 175-188.
Solow, A.R., Adams, R.F., Bryant, K.J., Legler, D.M., O'Brien, J.J., McCarl, B.A., Nayda, W., and Weiher, R. (1998). The value of improved ENSO prediction to U.S. agriculture. Climatic Change, 39, 47-60.


Study Chen, C.-C., McCarl, B. and Hill, H. (2002). Agricultural value of ENSO information under alternative phase definition. Climatic Change, 54, 305-325.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: adjust crop mix and storage levels
Dynamics: no
Time Scale: annual
Predictand: crop yield
Format: probabilistic
Type: realistic, derived
Quality Changes: yes
Baselines: climatological
VOI, imperfect: $399-$754 million (1996 US $)
VOI, perfect: $1390 million (1996 US $)
Risk Treatment: Expected value
Comments Economic value measured in terms of increase in total economic welfare and depends on number of phases in definition of El Nino phenomenon. Weather variables are implicit through variations in crop yields.
Related Studies Chen, C.C, and McCarl, B.A. (2000). The value of ENSO information: consideration of uncertainty and trade. Journal of Agricultural and Resources Economics, 25, 368-385.


Study Letson, D., Podesta, G.P., Messina, C.D., and Ferreyra, R.A. (2005). The uncertain value of perfect ENSO phase forecasts: Stochastic agricultural prices and intra-phase climatic variations. Climatic Change, 69, 163-196.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: crop mix (wheat, corn, sunflower, soybean, wheat/soybean rotation), variety, planting date, amount of nitrogen applied
Dynamics: no
Time Scale: daily
Predictand: maximum and minimum temperature, precipitation, radiation
Format: probabilistic
Type: realistic, derived
Quality Changes: no
Baselines: climatological
VOI, imperfect: $12.3 per hectare-year (1998 US $)
VOI, perfect: not reported
Risk Treatment: expected utility
Comments Consider Pampas region of Argentina. Assume perfect forecast of ENSO phase. Make case that forecast value is itself random because of imperfect relationship between ENSO phase and daily weather and because of uncertainty about crop prices.


Study Messina, C.D., Hansen, J.W., and Hall, A.J. (1999). Land allocation conditioned on ENSO phases in the Pampas of Argentina. Agricultural Systems, 60, 197-212.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: crop mix (corn, soybean, sunflower, wheat)
Dynamics: no
Time Scale: daily
Predictand: maximum and minimum temperature, precipitation, radiation
Format: probabilistic
Type: realistic, derived
Quality Changes: no
Baselines: climatological
VOI, imperfect: $5 - $15 per hectare-year (US$)
VOI, perfect: not reported
Risk Treatment: expected utility
Comments Forecast value depends on location, risk aversion, initial wealth, crop prices, and initial soil moisture. Constant relative risk aversion assumed.


Study Mjelde, J.W., Thompson, T.N., and Nixon, C.J. (1996). Government institutional effects on the value of seasonal climate forecasts. American Journal of Agricultural Economics, 78, 175-188.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: type of crop, amount of nitrogen applied, the farmer's participation in the Federal Farm Program, and whether to buy crop insurance
Dynamics: yes
Time Scale: seasonal
Predictand: precipitation
Format: probablistic
Type: Idealized
Quality Changes: yes
Baselines: climatological
VOI, imperfect: $974-$12,085 per farm (US $)
VOI, perfect: $16,567 per farm (US $)
Risk Treatment: expected value and expected utility
Comments Farm composed of 1,200 acres. Price changes would decrease forecast value. Risk-averse producer would value forecasts less. Crop insurance has little effect on forecast value, disaster program decreases forecast value, and farm program may increase or decrease forecast value.
Related Studies Mjelde, J.W., Thompson, T.N., Nixon, C.J., and Lamb, P.J. (1997). Utilising a farm-level decision model to help prioritise future climate prediction research needs. Meteorological Applications, 4, 161-170.


Study Solow, A.R., Adams, R.F., Bryant, K.J., Legler, D.M., O'Brien, J.J., McCarl, B.A., Nayda, W., and Weiher, R. (1998). The value of improved ENSO prediction to U.S. agriculture. Climatic Change, 39, 47-60.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: allocations among various crops
Dynamics: no
Time Scale: annual
Predictand: El Nino
Format: categorical
Type: realistic, derived
Quality Changes: yes
Baselines: climatological and persistence
VOI, imperfect: $240-$266 million/year (1995 US $)
VOI, perfect: $323 million/year (1995 US $)
Risk Treatment: expected value
Comments Forecast value for entire US based on expected economic surplus (i.e., sum of consumer and producer welfare). Uses teleconnection between El Nino events and minimum and maximum temperature and precipitation in US. Although El Nino forecasts exist, hypothetical assumptions are made about their skill.
Related Studies Adams, R.M., Bryant, K.J., McCarl, B.A., Legler, D.M., O'Brien, J.J., Solow, A.R., and Weiher, R. (1995). Value of improved long-range weather information. Contemporary Economic Policy, 13, 10-19.




Livestock



Bowman, P.J., McKeon, G.M., and White, D.H. (1995). An evaluation of the impact of long-range climate forecasting on the physical and financial performance of wool-producing enterprises in Victoria. Australian Journal of Agricultural Research, 46, 687-702.
Jochec, K.G., Mjelde, J.W., Lee, A.C., and Conner, J.R. (2001). Use of seaonal climate forecasts in rangeland-based livestock operations in West Texas. Journal of Applied Meteorology, 40, 1629-1639.


Study Bowman, P.J., McKeon, G.M., and White, D.H. (1995). An evaluation of the impact of long-range climate forecasting on the physical and financial performance of wool-producing enterprises in Victoria. Australian Journal of Agricultural Research, 46, 687-702.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: stocking and selling policies in sheep management
Dynamics: no
Time Scale: annual
Predictand: precipitation, temperature
Format: probabilistic
Type: idealized
Quality Changes: yes
Baselines: climatological
VOI, imperfect: $890-2,390 (Australian $)
VOI, perfect: $1,380-$2,940 (Australian $)
Risk Treatment: expected utility
Comments Farm composed of 500 hectares. Forecast value varies with location and level of quality. Decision rule is "conservative" (i.e., some degree of risk aversion) and suboptimal (in case of imperfect forecasts). Uses time series of daily weather data for given year.


Study Jochec, K.G., Mjelde, J.W., Lee, A.C., and Conner, J.R. (2001). Use of seaonal climate forecasts in rangeland-based livestock operations in West Texas. Journal of Applied Meteorology, 40, 1629-1639.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: stocking rates by livestock ranchers
Dynamics: no
Time Scale: seasonal
Predictand: temperature and precipitation
Format: probabilistic
Type: realistic, derived
Quality Changes: yes
Baselines: climatological
VOI, imperfect: -$149 to $5 per section-year (US $)
VOI, perfect: -$46 to $121 per section-year (US $)
Risk Treatment: see Comments
Comments Use decision rules based on input from focus group of decision makers (rather than maximizing expected value or utility). Forecast value depends on destocking price. Section = 259 hectares.
Related Studies Jochec, K. (2000). Economic viability of rangeland based ranching enterprises. M.S. thesis, Texas A&M University.




Wheat



Abawi, G.Y., Smith, R.J., and Brady, D.K. (1995). Assessment of the value of long range weather forecasts in wheat harvest management. Journal of Agricultural Engineering Resources, 62, 39-48.
Fox, G., Turner, J., and Gillespie, T. (1999). The value of precipitation forecast information in winter wheat production. Agricultural and Forest Meteorology, 95, 99-111.
Hammer, G.L., Holzworth, D.P., and Stone, R. (1996). The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability. Australian Journal of Agricultural Research, 47, 717-737.
Hill, H.S.J., Park, J., Mjelde, J.W., Rosenthal, W., Love, H.A., and Fuller, S.W. (2000). Comparing the value of Southern Oscillation Index-based climate forecast methods for Canadian and US wheat producers. Agricultural and Forest Meteorology, 100, 261-272.


Study Abawi, G.Y., Smith, R.J., and Brady, D.K. (1995). Assessment of the value of long range weather forecasts in wheat harvest management. Journal of Agricultural Engineering Resources, 62, 39-48.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: wheat harvest strategies such as early harvesting, drying, and contract harvesting
Dynamics: no
Time Scale: seasonal
Predictand: precipitation
Format: probablilistic
Type: realistic
Quality Changes: no
Baselines: climatological
VOI, imperfect: $12 per hectare-year (Australian $)
VOI, perfect: $20 per hectare-year (Australian $)
Risk Treatment: expected value
Comments Forecasts are based on statistical relationship between Southern Oscillation and precipitation. Decision rules are suboptimal, being based on heuristics.


Study Fox, G., Turner, J., and Gillespie, T. (1999). The value of precipitation forecast information in winter wheat production. Agricultural and Forest Meteorology, 95, 99-111.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: harvest timing
Dynamics: no
Time Scale: daily
Predictand: precipitation
Format: probabilistic
Type: realistic, derived
Quality Changes: yes
Baselines: persistence
VOI, imperfect: -$153 – +$364/hectare-year (Canadian $)
VOI, perfect: $0 – $364/hectare-year (Canadian $)
Risk Treatment: expected utility
Comments Optimizes drying costs & sprouting losses. Ex post approach. Uses mean-variance analysis to incorporate risk aversion. Value inversely related to degree of risk aversion.


Study Hammer, G.L., Holzworth, D.P., and Stone, R. (1996). The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability. Australian Journal of Agricultural Research, 47, 717-737.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: wheat crop management (choice of planting time, varietal development pattern, and fertilizer strategy)
Dynamics: no
Time Scale: seasonal
Predictand: precipitation and frost timing
Format: probabilistic
Type: realistic, derived
Quality Changes: no
Baselines: climatological
VOI, imperfect: up to 20% increase in profit (up to 35% reduction in risk)
VOI, perfect: 15% of value of perfect forecasts is achieved by present forecasts.
Risk Treatment: expected value and expected utility
Comments Forecasts are based on teleconnections with Southern Oscillation Index. Only one decision at a time treated, rather than simultaneous treatment of a sequence of decisions. "Risk" is defined as percentage of years with a net loss (i.e., profit is negative).


Study Hill, H.S.J., Park, J., Mjelde, J.W., Rosenthal, W., Love, H.A., and Fuller, S.W. (2000). Comparing the value of Southern Oscillation Index-based climate forecast methods for Canadian and US wheat producers. Agricultural and Forest Meteorology, 100, 261-272.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: fertilizer application level, planting date
Dynamics: no
Time Scale: seasonal
Predictand: precipitation, temperaure, and radiation
Format: probabilistic
Type: realistic, derived
Quality Changes: no
Baselines: climatological
VOI, imperfect: $0-$10/hectare-year (US $)
VOI, perfect: $9-$52/hectare-year (US $)
Risk Treatment: expected value
Comments Compares different definitions of ENSO phase. Value varies by site and crop price. Forecasts need to be tailored to specific regions.




Other



Anaman, K.A., and Lellyett, S.C. (1996). Assessment of the benefits of an enhanced weather information service for the cotton industry in Australia. Meteorological Applications, 3, 127-135.
Fox, G., Turner, J., and Gillespie, T. (1999). Estimating the value of precipitation forecast information in alfalfa dry hay production in Ontario. Journal of Production Agriculture, 12, 551-558.
Hill, H.S.J., Mjelde, J.W., Rosenthal, W., and Lamb, P.J. (1999). The potential impacts of the use of Southern Oscillation information on the Texas aggregate sorghum production. Journal of Climate, 12, 519-530.
Meza, F.J., and Wilks, D.S. (2003). Value of operational forecasts of seasonal average sea surface temperature anomalies for selected rain-fed agricultural locations of Chile. Agricultural and Forest Meteorology, 116, 137-158.
Wilks, D.S. and Wolfe, D.W. (1998). Optimal use and economic value of weather forecasts for lettuce irrigation in a humid climate.Agricultural and Forest Meteorology, 89, 115-130.


Study Anaman, K.A., and Lellyett, S.C. (1996). Assessment of the benefits of an enhanced weather information service for the cotton industry in Australia. Meteorological Applications, 3, 127-135.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: harvesting, chemical application, planting, irrigation, fertilizer application, etc.
Dynamics: no
Time Scale: daily
Predictand: temperature, wind, precipitation
Format: categorical and probabilistic
Type: realistic
Quality Changes: no
Baselines: basic public weather service
VOI, imperfect: $397,150 /year (1995 Australian $)
VOI, perfect: not reported
Risk Treatment: expected value
Comments Aggregate economic value (based on producers' surplus) of an enhanced weather service available for cotton producers in Australia. Total costs incurred by cotton producers for use of service are A$31,590/year. Based on contingent valuation technique, maximum amount user willing to pay averages A$223/year (compared to user fee of A$190/year).
Related Studies

Anaman, K.A., Thampapillai, D.J., Henderson-Sellers, A., Noar, P.F., and Sullivan, P.J. (1995). Methods for assessing the benefits of meteorological services in Australia. Meteorological Applications, 2, 17-29.

Anaman, K.A. and Lellyett, S.C. (1996). Producers' evaluation of an enhanced weather information service for the cotton industry in Australia. Meteorological Applications, 3, 113-125.

Anaman, K.A., et al. (1997). Benefits of Meteorological Services: evidence from recent research in Australia. Meteorological Applications, 5, 103-115.



Study Fox, G., Turner, J., and Gillespie, T. (1999). Estimating the value of precipitation forecast information in alfalfa dry hay production in Ontario. Journal of Production Agriculture, 12, 551-558.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: harvest timing
Dynamics: no
Time Scale: daily
Predictand: precipitation
Format: probabilistic
Type: realistic, derived
Quality Changes: yes
Baselines: persistence
VOI, imperfect: -$6 – +$36/acre-year (Canadian $)
VOI, perfect: $4 – $73/acre-year (Canadian $)
Risk Treatment: expected utility
Comments Optimize yield & quality. Ex post approach. Uses mean-variance analysis to incorporate risk aversion. Degree of risk aversion not an important determinant of value.


Study Hill, H.S.J., Mjelde, J.W., Rosenthal, W., and Lamb, P.J. (1999). The potential impacts of the use of Southern Oscillation information on the Texas aggregate sorghum production. Journal of Climate, 12, 519-530.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: cultivar type, fertilizer application level, planting date, and seeding rate
Dynamics: yes
Time Scale: seasonal
Predictand: precipitation, temperature, and radiation
Format: probabilistic
Type: realistic, derived
Quality Changes: no
Baselines: climatological
VOI, imperfect: $0-$90/hectare (US $)
VOI, perfect: not reported
Risk Treatment: expected value
Comments Forecast value depends on site, phase of Southern Oscillation, and crop price. Forecast is based on teleconnections with Southern Oscillation.


Study Meza, F.J., and Wilks, D.S. (2003). Value of operational forecasts of seasonal average sea surface temperature anomalies for selected rain-fed agricultural locations of Chile. Agricultural and Forest Meteorology, 116, 137-158.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: planting date and density, nitrogen fertilization level
Dynamics: no
Time Scale: seasonal
Predictand: sea surface temperature (ENSO)
Format: probabilistic
Type: realistic
Quality Changes: no
Baselines: climatological
VOI, imperfect: wheat: 22% to 44% of value of perfect information; potato: 25% to 55% of value of perfect information
VOI, perfect: wheat: $12.5 to $15.1 per hectare-year (US $); potato: $19.3 to $243.0 per hectare-year
Risk Treatment: expected value
Comments Daily weather (maximum, minimum, and dew point temperature; wind speed) generated by stochastic weather generator conditional on ENSO state. Forecast value depends on location and soil type. Forecasts based on operational ENSO forecasting model (ENSO-CLIPER), with smaller economic value being obtained for two simpler statistical forecasting models.
Related Studies Meza, F.J., Wilks, D.S., Riha, S.J., and Stedinger, J.R. (2003). Value of perfect forecasts of sea surface temperature anomalies for selected rain-fed agricultural locations of Chile. Agricultural and Forest Meteorology, 116, 117-135.


Study Wilks, D.S. and Wolfe, D.W. (1998). Optimal use and economic value of weather forecasts for lettuce irrigation in a humid climate.Agricultural and Forest Meteorology, 89, 115-130.
Structure of Decision Problem Forecast Characteristics Information Valuation
Decision: irrigation timing
Dynamics: yes
Time Scale: 1 and 2-days
Predictand: temperature, precipitation
Format: categorical and probabilistic
Type: realistic, derived
Quality Changes: no
Baselines: conventional decision rule
VOI, imperfect: $900-1000 hectare/year (US $)
VOI, perfect: not reported
Risk Treatment: expected value
Comments Treats serial correlation. Conventional, non-optimal decision rule based on soil moisture (also consider rule of never irrigating). Day 2 forecasts contribute substantially to overall forecast value.


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