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Adaptively Managing Agricultural Production for Future Climate Change in Montana’s Flathead County Zeyuan Qiu, New Jersey Institute of Technology Tony Prato, University of Missouri Dan Fagre, USGS Tony Prato (PI), ecological economics, University of Missouri Zeyuan Qiu (co-PI), natural resource and environmental economics, New Jersey Institute of Technology Dan Fagre (co-PI), ecology and climate science, USGS Northern Rocky Mountain Science Center Duane Johnson, agronomy, Great Plains Oil & Exploration 1. To construct plausible future climate scenarios for Montana’s Flathead Valley. 2. To develop an Adaptive Agricultural Management (AGGEM) model to: (a) evaluate the impacts of climate change on crop production; and (b) determine how best to adapt crop production systems to future climate change for representative farms in the Flathead Valley. 3. To create an interactive spatial decision support tool (ISDST) that makes the AG-GEM model and associated geospatial databases useable and accessible to farmers and agricultural technical service providers. Study Area Flathead Valley 2,000 square miles When compared to the 20th century average, the West has experienced an increase in average temperature during the last five years that is 70 percent greater than the world as a whole. Crop yields are expected to increase in crop production areas that experience slightly higher surface air temperature and higher summer precipitation, and decrease in production areas that experience significantly higher surface air temperature, lower summer precipitation, and reduced irrigation water supplies. Other things equal, crop yields increase with the higher atmospheric CO2 concentration. Crop farmers can take advantage of the benefits and reduce the risks of future climate change by adapting their crop production systems to actual or expected climate change. Representative farms and producer panels Crop production systems Evaluation and ranking of crop production systems Simulating crop yields, soil erosion, and water quality Determination of best crop production systems Adaptive management of crop farms for climate change Assessing potential benefits of adaptive management of crop farms Two representative farms have been defined for Flathead Valley. Two representative farms: 164 acres (small; farm at NW Montana Agr Res Ctr) 600 acres (large) Representative farms differ in terms of: (1) size (small and large); (2) tenure (acres owned and leased) and asset values; (3) crop enterprises; (4) target crop yields; (5) mix of dryland and irrigated crops; (6) variable and fixed costs of crop production; (7) machinery complement and replacement strategy; & (8) participation in federal farm programs. A producer panel was established for each representative farm that consists of one to three farmers that operate farms in Flathead Valley that have features similar to the representative farm. Each producer panel assists the research team in: (1) identifying the eight features of the representative farm listed above; (2) providing other information required to implement the AG-GEM model; and (3) formulating alternative crop production systems and adaptive strategies for a representative farm. The first meeting of the producer panels was held in February 2007. At that meeting the producer panels were introduced to the project and asked to provide information about historical crop production systems and various parameters of the AG-GEM model. A second meeting of the producer panels will be held this year to present and discuss modeling results for the future climate period and their willingness to adopt various strategies for adapting crop production systems to future climate change. A crop production system consists of a particular mix of crop enterprises (i.e., acreages planted to different crops) for a representative farm. Historical crop production systems incorporate crop enterprises that are suitable to growing and climatic conditions in the local production area during the historical climate period. Historical climate period: 1960-2006 (46 years) Small Farm 30 acres of spring wheat (54 bu/acre) 55 acres of irrigated alfalfa (5 tons/acre) 15 acres of spring canola (40 bu/acre) 4 acres of camelina (40 bu or 2,000 lb/acre) 20 acres of irrigated peppermint (85 lb/acre) 40 acres of permanent pasture 164 acres Large Farm 200 acres of winter wheat (75 bu/acre) 150 acres of spring barley (80 bu/acre) 100 acres of spring canola (2,000 lb/acre) 150 acres of dryland hay (3.5 tons/acre) 600 acres Future crop production systems incorporate specific strategies for adapting the best historical crop production system to climate change in the future climate period. Future climate period: 2007-2053 (46 years) Crop enterprises and adaptive strategies are identified by the research team and producer panels. Potential adaptive strategies include: using later maturing cultivars to take advantage of longer growing seasons; planting crops earlier and using higher seeding rates to take advantage of higher spring temperatures and higher precipitation; changing crop enterprises; adopting new crops, including those that are more drought tolerant; shifting the timing and scheduling of field operations to take advantage of earlier planting dates and a longer growing season; reducing use of irrigation water due to higher precipitation; improving irrigation efficiency; altering nutrient and pesticide management practices in response to higher temperatures and greater precipitation; increasing crop drying and pesticide use in response to hotter and wetter summers; and increasing field drainage in response to higher precipitation Crops that were used to develop three alternative, historical crop production systems for the two representative farms: barley peppermint oats lentils spring wheat permanent pasture winter wheat irrigated hay spring canola dryland hay winter canola dryland alfalfa camelina irrigated alfalfa The AG-GEM model evaluates and ranks crop production systems for each representative farm based on four attributes of those systems: annual net farm income; variance in annual net farm income; annual soil erosion; and annual water quality (e.g., nitrogen and phosphorus in runoff). The ranking procedure requires the weights for the four attributes. The producer panels were asked to assign weights to the attributes, such that the sum of the weights equals one. The EPIC model is being used to simulate crop yields, soil erosion, and water quality for each representative farm for two soil regimes: a moderately favorable soil regime; and a highly favorable soil regime. EPIC-simulated crop yields for the future climate period are increased 2% per annum to account for increases in crop yields due to technological change. The daily weather inputs for the EPIC model include precipitation, maximum and minimum temperatures, relative humidity, solar radiation, and wind velocity. These data are readily available for the historical climate period. Daily precipitation and temperature for the future climate period are derived by adjusting daily precipitation and temperature data for the historical climate period using monthly precipitation and temperature data for the future climate period. The latter are from a 24-model, 12-km resolution, dataset for the future climate period developed by the Coupled Model Intercomparison Project (CMIP3). • The CMIP3 dataset covers three CO2 emission scenarios: Daily relative humidity and solar radiation for the future climate period are derived from the estimated daily precipitation and temperature for that period and geographic data for the study area. Daily wind velocity in the future climate period is assumed to be same as in the historical climate period. The best crop production systems for a representative farm in both the historical and future climate periods will be determined using a multiple-attribute decision model that integrates the Technique for Order Preference by Similarity of Ideal Solution (TOPSIS) method and the stochastic dominance with respect to a function (SDRF) criterion. The TOPSIS method is used to calculate the relative closeness of feasible crop production systems to the positive-ideal solution (closeness coefficient for short) for each representative farm. The SDRF criterion ranks the feasible production systems for a representative farm by applying the SDRF criterion to sample values of the closeness coefficients for those systems. The ranking assumes the risk attitudes indicated by the two producer panels, namely risk loving for the small representative farm and risk neutral for the large representative farm. The best crop production system for a representative farm is the highest ranked feasible crop production system for that farm. The adaptive management framework in the AG-GEM model incorporates both ex ante and ex post adaptive management. Ex ante adaptive management determines how a farmer adapts the best historical crop production system for a representative farm to each of the three climate scenarios in the future climate period (i.e., prior to future climate change). The alternative future crop production systems evaluated with ex ante adaptive management incorporate one or more strategies identified by the producer panel for adapting the best historical crop production system to future climate change. The best future crop production system for a climate scenario is determined by applying a multiple-attribute decision model to the alternative future crop production systems for that scenario. Ex post adaptive management dynamically adjusts crop production systems for a representative farm to emerging changes in climate in consecutive time intervals (e.g., every five years). Ex post adaptive management takes place in real time and, as such, is applicable only as future climate change occurs. The potential benefits of adapting best historical crop production systems for representative farms to future climate change is assessed by ranking two alternative crop production schemes: (1) using the best crop production system for the historical climate period in the future climate period, which implies no adaptation to future climate change (first scheme); and (2) using the best crop production system for each climate scenario for the future climate period (second scheme), which is equivalent to ex ante adaptive management. If, for a particular climate scenario and representative farm, the best crop production system with adaptation (scheme 2) ranks higher than the best crop production system without adaptation (scheme 1), then adaptation is a superior to non-adaptation for that scenario and farm. Conversely, if, for a particular climate scenario and representative farm, the best crop production system without adaptation (scheme 1) ranks higher than the best crop production system with adaptation (scheme 1), then adaptation is not superior to non-adaptation for that scenario and farm. To date, no studies have evaluated: (1) the potential impacts of future climate change on net farm income and the quality of natural resources used in crop production for representative crop farms in a local production area; (2) how best to adapt those farms to future climate change; and (3) whether such adaptation is advantageous. This situation exists due to: (1) a lack of integrated models with which to assess the economic and environmental impacts of climate change on representative farms and adaptation of representative farms to climate change; and (2) a lack of higher resolution spatial data with which to simulate the crop production impacts of future climate scenarios. The AG-GEM model alleviates the first deficiency and the CMIP3 dataset alleviates the second deficiency. Questions/Comments http://cares.missouri.edu/agclim-montana