that factors such as high yielding seeds, use of chemical fertilizer and types of sowing tech-
niques all reduce vulnerability to drought and flooding over large areas, causing some regions
to be far more sensitive to changes in precipitation patterns than others (Hansen et al ., 2011;
Challinor et al ., 2007; Simelton et al ., 2012).
The 2001 Intergovernmental Panel on Climate Change (IPCC) report defined vulnerability
as a function of exposure, impact and adaptive capacity. Underlying socio-economic factors
may work to increase the adaptive capacity to withstand droughts, buffering harvests from the
effects of adverse weather. Thus the context in which a farmer is working will either enhance
or reduce a farmer's ability to withstand climate conditions, affecting the society's overall vul-
nerability. This is a critical point, since many developed-world economists and analysts under-
estimate the impact of weather on agriculture, since it is often difficult to relate in a linear
fashion the impact of a particular level of drought to a specific reduction in yield. The work
done by Simelton and others (2012) brings a level of quantitative analysis to understanding how
soil moisture and growing conditions through time affect ultimate production.
Climate variability and change affects and is caused by agricultural activities at the global scale.
Land conversion, forest fragmentation, deforestation and soil degradation at a global scale has
had profound effects on the climate due to the domestication of the land surface as the popu-
lation has expanded. Climate variability, through droughts and floods, continues to have a
profound effect on agricultural production despite the use of technology. This chapter
explored the interaction between agriculture and growing conditions, through the use of
satellite remote sensing and agricultural statistics during the past three decades. All over the
world, seasons are changing, starting earlier or later, lengthening or shifting, affecting tradi-
tional agricultural systems and management strategies for farmers. The connection between
weather and agricultural productivity was explored using country-level analyses, and how
these trends are related to climate variability was presented.
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U.,
Curtis, S., Bolvin, D., Gruber, A., Susskind, J. and Arkin, P. (2003) The version 2 global precipita-
tion climatology project (GPCP) monthly precipitation analysis (1979-present). Journal of Hydromete-
orology , 4, 1147-1167.
Atzberger, C. (2013) Advances in remote sensing of agriculture: Context description, existing opera-
tional monitoring systems and major information needs. Remote Sensing Journal , 5, 949-981.
Barrett, C. B. and Maxwell, D. G. (2005) Food aid after fifty years: Recasting its role , New York,
Bindraban, P. S. and Rabbinge, R. (2012) Megatrends in agriculture: Views for discontinuities in past
and future developments. Global Food Security , 1, 99-105.
Bolten, J. D., Crow, W. T., Zhan, X., Jackson, T. J. and Reynolds, C. (2010) Evaluating the utility of
remotely sensed soil mositure retrievals for operational agricultural drought monitoring. IEEE Journal
of Selected Topics in Applied Earth Observations and Remote Sensing , 3, 57-66.
Brown, M. E. (2008) Famine early warning systems and remote sensing data , Heidelberg, Springer Verlag.
Brown, M. E., de Beurs, K. M. and Marshall, M. (2012) Global phenological response to climate change
in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26 years.
Remote Sensing of Environment , 126, 174-183.