Agriculture Reference
In-Depth Information
based on farmer interviews not biophysical analyses. Long-term data records derived from
satellite remote sensing can be used to verify these reports, providing necessary analysis and
documentation required to plan effective adaptation strategies with farmers. Earth science can
also provide some understanding of whether these changes are likely to continue and their
spatial extent.
Early research on the impact of global climate change on the growing season in northern
latitudes was based on satellite remote sensing observations of vegetation (Myneni
et al
., 1997;
Nemani
et al
., 2003; Slayback
et al
., 2003). These direct observations of change in the onset
of spring led to the development of phenological models using remote sensing information.
Phenology is the study of the timing of recurring biological cycles and their connection to
climate (Lieth, 1974). Phenology, has the promise of capturing quantitatively the changes
reported by farmers and providing evidence for their link to climate change.
White
et al.
(2009) described the complexity of comparing ground observations of the start
of season with satellite-derived estimates due to the difficulty in understanding the myriad
definitions of season metrics. This study compared the different models and methods of deriv-
ing phenological metrics from remote sensing datasets, and how the results are strongly related
to ground-based phenology and to processes such as changes in snow cover, soil thaw, ice and
hydrology. The study highlights both the challenge and potential for integrating remote
sensing and ground observations. No other technology besides remote sensing offers wall-to-
wall coverage and consistent daily long-term monitoring, yet few metrics of biospheric
response are as unconstrained by appropriate ground data on changes in spring onset as those
focused on determining the start of season (White
et al
., 2009). Although the study revealed
complexities in the monitoring of growing season land surface response to the impact of
climate variability during the relatively short 25-year record used, continued reports from the
widespread changes in seasonality drive the need for improved methods and research using
remote sensing (de Beurs and Henebry, 2010; Korner and Basier, 2010).
Land surface phenology models rely on remote sensing information of vegetation, such as
the dataset derived from the AVHRR (Tucker
et al
., 2005) and the newer MODIS sensors
on Aqua and Terra. Vegetation and rainfall data can assess variables such as the start of season,
growing season length and overall growing season productivity (Brown, 2008). These metrics
are common inputs to crop models that estimate the impact of weather on yield (Verdin and
Klaver, 2002). Phenology metrics have a strong relationship with regional food production,
particularly those with sufficiently long records to capture local variability (Funk and Budde,
2009a; Vrieling
et al
., 2008).
Plate 9
shows a map of the start of season and length of season trends over the 26 years of
the start of the season and peak position. This plate shows regions with earlier starts (in red)
and later starts (in blue). The bottom panel shows regions with longer growing seasons (in
red) and shorter seasons (in blue). Uncertainty in the timing of the agricultural season can be
very difficult to adjust to, if communities could previously plan on the rains starting at exactly
the same time every year. These changes are likely to have an impact on agriculture, but what
the impact is on food security depends on the context in which the long-term trends occur
and how farmers manage the changes.
Given the agricultural nature of many developing country economies, agricultural produc-
tion continues to be a critical determinant of both food security and economic growth (Funk
and Brown, 2009). Crop phenological parameters, such as the start and end of the growing
season, the total length of the growing season, and the rate of greening and senescence are
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