Wesley Rosenthal

Comparing the value of Southern Oscillation Index-based climate forecast methods for Canadian and US wheat producers

Southern Oscillation Index (SOI) based forecasting methods are compared to determine which method is more valuable to Canadian and US wheat producers. Using decision theory approach to valuing information, the more commonly used three-phase method of El Nino, La Nina, and other is compared to a five-phase system. Because of differences in growing season and yearly SOI classification schemes, two different three-phase methods are used. The five-phase system is based on the level and rate of change of the SOI over a 2 month period. Phases are consistently negative, consistently positive, rapidly falling, rapidly rising, and near zero. As expected, results vary by the method used. Winter wheat producers in Illinois place no value on either of the SOI-based forecasting systems. Producers at seven of the 13 sites prefer the five-phase method over either of the three-phase method (spring wheat producers in Manitoba, Alberta, North Dakota and South Dakota, along with winter wheat producers in Oklahoma, Texas, and Washington). The value of the five-phase approach is up to 70 times more valuable than the three-phase approach. Producers growing spring wheat in Saskatchewan and Montana, along with winter wheat producers in Ohio and Kansas value the three-phase approach more than the five-phase. In this case, the value of the three-phase system is up to two times more valuable than the five-phase system. Depending on expected price and region, the values of the SOI-based forecasts range from 0 to 22% of the value of perfect forecasts. In both absolute and percentage of perfect forecasts, producers in Oklahoma, Texas, Manitoba, Saskatchewan, and South Dakota value either system more than producers in the remaining regions. Economic value and distributional aspects of the value of climate forecasts have implications for producers, policy makers, and meteorologists. Finally, the results clearly suggest all producers will not prefer one forecast type. Forecasts need to be tailored to specific regions.

The Potential Impacts of the Use of Southern Oscillation Information on the Texas Aggregate Sorghum Production

Economic decision models incorporating biophysical simulation models are used to examine the impact of the use of Southern Oscillation (SO) information on sorghum production in Texas. Production for 18 sites is aggregated to examine the impact of the use of SO information on the aggregate supply curve and other production and economic variables. Two information scenarios are examined. For all expected prices, the use of SO information increased producers’ net returns over the scenario in which SO information is not used. Depending on price, the expected Texas aggregate sorghum supply curve using SO information shifted both left and right of the without SO information supply curve. Changes in nitrogen use based on the SO information is a major factor causing the shift in the supply curves. Further, the use of SO information decreased aggregate expected costs per metric ton of production. Changes associated with the use of SO information can be summarized as follows: the use of SO information provides producers a method to use inputs more efficiently. This more efficient use has implications for both the environment and for the agricultural sector.

Predicting regional grain sorghum production in Australia using spatial data and crop simulation modelling

Grain sorghum is the major dryland summer crop produced in the subtropical region of Australia. Production variability is great and the consequent uncertainty about likely production restricts marketing options and contributes to instability in grain price. Improved methods for predicting regional production would assist marketing decisions for farmers and grain traders. The objective of this study was to determine whether reliable regional grain sorghum production predictions could be generated by combining crop simulation and geographic information system technologies. We used historical shire production data to test the approach using hindcasting. Geographical data bases of landscape and soil attributes were used to define arable land boundaries and soil properties. Geographical data bases of daily rainfall and climate were overlaid and used to drive a spatial simulation of sorghum production for all shires in Queensland for the period 1977-1988. The results of the simulation were compared with production statistics at the shire and aggregate state levels. The spatial integrity of the prediction system was examined by comparing maps of predicted and reported shire production for specific years. There was a general tendency for the simulated yields per unit area to be greater and more variable (from year to year) than the historical shire production data. This probably reflected the fact that the simulation assumed perfect management and pest-free conditions. The relatively coarse spatial interpolation of rainfall would likely also contribute to this outcome. Linear regression relationships were developed between historical and simulated data at the shire scale to calibrate the simulated yields. Estimates of total production for each shire (in any year) were derived from the predicted yield per unit area, which was derived from the regression correction of the simulated yield, and the reported area planted. Excellent agreement between predicted and reported production occurred both at the individual shire and aggregate state (r = 0.96) scales. This approach was compared with use of mean shire yield as the estimate of predicted yield per unit area to examine the contribution of the yield simulation procedure to production prediction. Significant improvement in production prediction was attributed to the yield simulation. The spatial distribution of shire production estimates was examined by categorising and mapping shire production predictions. Comparisons with reported production estimates showed the integrity of the spatial distribution was largely retained. Hence, we conclude that reliable shire and state sorghum production estimates can be generated by combining crop simulation and geographic information system technologies. The procedure is suitable for further development for use in real-time. By updating estimates as a season progresses, improved timeliness and accuracy of production forecasts could be achieved.

Visible/Infrared/Microwave Agriculture Classification, Biomass, and Plant Height Algorithms

This paper describes the results of a study to determine if visible, infrared, and microwave data is correlated to crop-canopy characteristics (biomass and crop height) and can improve estimates of crop acreage. The objectives were to 1) determine if different crops can be discriminated using multifrequency microwave data, and 2) determine which visible, infrared, and microwave spectral regions can classify crops and correlate well to plant biomass, crop height, and the perpendicular vegetation index (PVI). The study was conducted at Dalhart, Texas, in 1980. Aircraft multispectral data collected during the study included visible and infrared data and active multifrequency microwave data. Ground-truth data from each field consisted of soil moisture, total plant biomass, and crop height. Results indicated that C- (4.75 GHz), L- (1.6 GHz), and P- (0.4 GHz) band active microwave data combined with visible and infrared data maintained or improved crop-discrimination accuracy compared to models using only visible and infrared data. The active microwave frequencies were more sensitive to plant height differences than total biomass differences; the K- (13.3 GHz) and C-bands were sensitive to height variations in short plants, while the P-band was sensitive to differences between tall and short plants.