Changing water availability during the African maize-growing season, 1979–2010

Abstract

Understanding how global change is impacting African agriculture requires a full physical accounting of water supply and demand, but accurate, gridded data on key drivers (e.g., humidity) are generally unavailable. We used a new bias-corrected meteorological dataset to analyze changes in precipitation (supply), potential evapotranspiration (E p , demand), and water availability (expressed as the ratio P/E p ) in 20 countries (focusing on their maize-growing regions and seasons), between 1979 and 2010, and the factors driving changes in E p . Maizegrowing areas in Southern Africa, particularly South Africa, benefitted from increased water availability due in large part to demand declines driven primarily by declining net radiation, increasing vapor pressure, and falling temperatures (with no effect from changing windspeed), with smaller increases in supply. Sahelian zone countries in West Africa, as well as Ethiopia in East Africa, had strong increases in availability driven primarily by rainfall rebounding from the long-term Sahelian droughts, with little change or small reductions in demand. However, intraseasonal supply variability generally increased in West and East Africa. Across all three regions, declining net radiation contributed downwards pressure on demand, generally over-riding upwards pressure caused by increasing temperatures, the regional effects of which were largest in East Africa. A small number of countries, mostly in or near East Africa (Tanzania and Malawi) experienced declines in water availability primarily due to decreased rainfall, but exacerbated by increasing demand. Much of the reduced water availability in East Africa occurred during the more sensitive middle part of the maize-growing season, suggesting negative consequences for maize production.

Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier

“…In the first step, we used the decisionsupport system for agrotechnology transfer (DSSAT, [26]) to simulate maize and soya bean yields at the sites of 40 weather stations distributed across Zambia. They are the points where a 31-year, gridded daily meteorological dataset has received the greatest amount of bias-correction and infilling by weather observations [27][28][29]. We used soil profiles corresponding to those locations drawn from the WISE v. 1.1 gridded soil profile database [30], and simulated for each location maize yields over all 31 years under commercial input practices for three to four cultivars representative of short and medium season length according to the Zambian crop calendar [31].…”

Dominance of climate warming effects on recent drying trends over wet monsoon regions

“…PET variations largely affect dryness trends that are in turn closely related to the occurrence of droughts, water scarcity, and tree mortality (Westerling et al, 2006;Park Williams et al, 2013;Dai, 2013). Drying impacts of PET increase are usually emphasized in water-limited regions (Westerling et al, 2006;Estes et al, 2014); however, humid areas are also expected to experience severe aridification in the 21st century because of a continuous increase in PET (Feng and Fu, 2013;Cook et al, 2014). Thus, the processes involved in the variability in dryness need to be examined in various hydroclimate regimes to better understand continental dryness changes.…”

“…In this study, an aridity index, PET /P , defined as PET based on the Penman-Monteith method (Allen et al, 1998) divided by P , is employed to assess surface dryness and its trends (Estes et al, 2014;Greve et al, 2014). Over the land surface, the amount of actual evaporation (AET) is con- strained by the amount of P , which is also generally less than PET because of limited available water at the surface (Fu and Feng, 2014;Greve et al, 2014).…”

A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes

“…Finally, our approach has shown the ability to generalize well across large areas and disparate agricultural land cover types, paving the way for its large-scale deployment in which new images are classified using a curated database of hand-digitized field boundaries. In addition to providing high-resolution maps of agricultural land cover and reducing uncertainties in existing data sets, applying this algorithm over Sub-Saharan Africa can provide a critical and improved constraint for regional crop productivity [105,106] and agro-climatological assessments [107], as well as assessments of land cover change.…”