Agriculture Reference
In-Depth Information
important for planning crop management and crop diversification and intensification.
Research to calculate the average and annual start of season for agricultural areas in all regions
is ongoing.
As phenological models are implemented in regions that have temperature controlled,
complex, rapidly changing and experiencing much larger climate impacts than in Africa,
results will not be as readily explainable as those found in tropical agroecosystems. White et
al. (2009) focused on North America and did not use agricultural data, they found no evid-
ence for trends that indicated an earlier spring arrival. Using an ensemble estimate from two
land surface phenology methods that were more closely related to ground observations than
other methods, start of season trends could be detected for only 12 percent of North America
and were divided between trends towards both earlier and later spring onset.
There are widespread reports of significant and challenging seasonality changes in many
agricultural regions, documenting these changes will require a concerted effort. Establishing
consistent plant phenology monitoring networks (e.g., the USA National Phenology
Network, , or the European Phenology Network) as well as incorporating
a broader consideration of non-climatic factors influencing start of season such as land use
change, soil fertility and altered crop distribution is therefore critical in improving our under-
standing of the possible negative impact of climate change on agriculture in the coming
Using phenology to estimate production during a food security crisis
Phenology is not just useful for understanding widespread trends in the growing season, it can
be used directly to estimate changes in food production. In 2006-08, USAID needed more
information about actual food production in Zimbabwe. A prolonged drought, combined
with reductions in cropped area and use of agricultural inputs led to a 55 percent cereal pro-
duction shortfall (Funk and Budde, 2009b). These reductions, together with the world's
highest inflation rate and 80 percent unemployment, led to the dire need of food aid donors
to know the appropriate amount of food to send, which required a very accurate food pro-
duction estimate.
Funk and Budde (2009) used the method to estimate yield and production declines due to
weather in Zimbabwe in 2008. The approach focused on yield-vegetation relationships that are
typically strongest after mid-season. Rasmussen found that NDVI values from the mid to the
end of the growing season in Burkina Faso explained 93 percent of millet yields (Rasmussen,
1992). Authors have taken a number of approaches to increase the correlation between NDVI
and yields, most commonly using a simple mask to remove non-agricultural vegetation signals
from a time series analysis (Genovese et al ., 2001; Kastensa et al ., 2005). Funk and Budde (2009)
focus on developing a phenologically adjusted, crop masked, NDVI time series analysis to visu-
alize and summarize agricultural performance in Zimbabwe. The resulting analysis showed a
strong relationship to agricultural production figures from the US Department of Agriculture,
enabling both a spatial and temporal assessment of production (Funk and Budde, 2009b).
This approach was particularly necessary in a place like Zimbabwe due to the recent change
in its agricultural regime. In 2006-07, poor growing conditions combined with reductions in
cropped area and a transformation of the agriculture sector due to government land distribution
efforts resulted in extremely low food production (Scoones et al ., 2011). The impact of land
reform in the region was complex and diverse, which created a large change in management
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