Climate Impacts On Agriculture in the United States: Understanding the Value of Past Observations.

Climate impacts on agriculture are observed in a variety of ways, either through reduced biomass or grain yield. Within the United States we are fortunate to have an extensive data base of crop yields by state and county level for nearly 100 years and access to a well-maintained and available climatic parameter network that allows for detailed assessments of the relationship of climate to crop yield. An additional benefit of this extensive data base is the wide variety of crops that have been archived. These data can be easily assembled into geographical information system platforms to allow for detailed spatial analysis of the changes in crop yield relative to the changes in climate variation. These data sets allow for an examination of the large impacts of climate, e.g., drought, late frost, early frost, or excessively wet conditions on crop growth. These types of analyses are critical to being able to ascribe the major impacts of climate on agricultural production. However, these data are not sufficient for the detailed analysis required to assess the relationships that define how well adaptive management strategies may be performing. As an example, the increase in nighttime temperatures during the grain-filling period and the resultant impact on the length of the growing season will require observations of the onset and cessation of the reproductive period of grain crops. This could be obtained from remote sensing methods if the temporal frequency and spatial resolution were sufficient to allow for these analyses. To assess the impacts of climate change on the physiological responses of crops will require more detailed observations of the within season growing conditions and the physiological responses of the crop, e.g., photosynthesis, biomass accumulation, water stress, etc. To address the problem of being able to quantify the response of crops to climate change will require an integrated effort of experimental and simulation models to understand the dynamics of cropping systems so that we can ensure food security for future generations.