Agriculture Reference
In-Depth Information
climate change leading to further warming of global mean temperatures and shifts in
precipitation patterns, these are not the only climatic processes which may influence crop and
also soybean production. Climate models used to drive crop models may, therefore, need to
consider changes in the land surface, either as imposed boundary conditions or as feedbacks
from an interactive climate-vegetation model. Crops may also respond directly to changes in
atmospheric composition, such as the concentrations of carbon dioxide (CO
2
), ozone (O
3
) and
compounds of sulphur and nitrogen, so crop models should consider these processes as well
as climate change. Changes in these, and the responses of the crops, may be intimately linked
with meteorological processes so crop and climate models should consider synergies between
climate and atmospheric chemistry. Some crop responses may occur at scales too small to
significantly influence meteorology [11,5,12], so may not need to be included as feedbacks
within climate models. However, the volume of data required to drive the appropriate crop
models may be very large, especially if short-time-scale variability is important.
Implementation of crop models within climate models would minimize the need to transfer
large quantities of data between separate modeling systems. It should also be noted that crop
responses to climate change may interact with other impacts of climate change, such as
hydrological changes. For example, the availability of water for irrigation may be affected by
changes in runoff as a direct consequence of climate change, and may also be affected by
climate-related changes in demand for water for other uses. It is, therefore, necessary to
consider the interactions between the responses of several impacts sectors to climate change.
Overall, there is a strong case for a much closer coupling between models of climate, crops
and hydrology, but this in itself poses challenges arising from issues of scale and errors in the
models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-
crop-hydrology model is paralleled by continued use of separate climate and SVAT but with
a focus on consistency between the models [13-17]
Crop models and SVAT models have been designed for analyzing the interactions
between plant canopy processes and the environment. They give priceless information for
production and yield monitoring, management of water resources, assessment of water
requirements and more recently for carbon cycle studies in relation with climate research. The
use of such models over large areas is limited by our ability to provide them with the required
input information. On one hand, it is almost impossible to obtain requisite plant and soil
characteristics directly from networks of ground observations. On the other hand, remote
sensing techniques can provide information on plant canopy processes that may be used for
driving or constraining crop and SVAT models over large areas. These models may be
operated without a systematic use of remote sensing data by intrinsically providing the means
for interpolating energy and water fluxes or biomass production between remote sensing data
acquisitions [18]. Compared to classical methods for mapping evapotranspiration, based on
models such as SEBAL [19,20], the use of crop or SVAT models makes it possible to
continuously monitor evapotranspiration along the whole crop cycle instead of estimating it
for only snapshots derived from images.
Search WWH ::
Custom Search