Scientists at CHG develop a wide range of computer based models to predict food insecurity and famine conditions months ahead of time. These predictions help policy makers and aid agencies to prepare for food scarcity and allocate resources accordingly.
District-level crop area (CA) is a highly uncertain term in food production equations, which are used to allocate food aid and implement appropriate food security initiatives. Remote sensing studies typically overestimate CA and production, as subsistence plots are exaggerated at coarser resolution, which leads to over optimistic food reports. In this study, medium resolution Landsat ETM+ images were manually classified for Niger and corrected using CA estimates derived from high resolution sample image, topographic, and socioeconomic data. A logistic model with smoothing splines was used to compute the block-average (0.1 degree) probability of an area being cropped. Livelihood zones and elevation explained 75% of the deviance in cropped area, while medium resolution did not add explanatory power. The model overestimates crop area when compared to the national inventory, perhaps due to temporal changes in intercropping, and the exclusion of some staple crops in the national inventory.
This paper examines intraseasonal changes in maize phenology and heat stress exposure over the 1979–2008 period, using Mozambique meteorological station data and maize growth requirements in a growing degree-day model. Identifying historical effects of warming on maize growth is particularly important in Mozambique because national food security is highly dependent on domestic food production, most of which is grown in already warm to hot environments. Warming temperatures speed plant development, shortening the length of growth periods necessary for optimum plant and grain size. This faster phenological development also alters the timing of maximum plant water demand. In hot growing environments, temperature increases during maize pollination threaten to make midseason crop failure the norm. In addition to creating a harsher thermal environment, we ﬁnd that early season temperature increases have caused the maize reproductive period to start earlier, increasing the risk of heat and water stress. Declines in time to maize maturation suggest that, independent of effects to water availability, yield potential is becoming increasingly limited by warming itself. Regional variations in effects are a function of the timing and magnitude of temperature increases and growing season characteristics. Continuation of current climatic trends could induce substantial yield losses in some locations. Farmers could avoid some losses through simple changes to planting dates and maize varietal types. [Clim Res (2011)]
The most recent IPCC report on climate change provided a critical consensus on anthropogenic long term warming, globally. Now, the research focus must turn to predicting regional and local effects of climate change. The CHG engages in research activities aimed at predicting the effects of climate change on specific food producing regions and localities throughout the developing world. Predictive science carried out at CHG is used by researchers and technologists to develop effective climate change adaptation strategies, and by policy makers to allocate climate change adaptation resources.
Guatemala and Haiti are two of the most food insecure nations in the Western Hemisphere. Measurements of food availability and access are instrumental in developing targeted hunger reduction strategies yet no estimates of cropped area (a critical input in the calculation of food production) at either a national or sub-national-level exist. The purpose of this research is to produce estimates of cropped area for Guatemala and Haiti using an area frame sampling approach and very high resolution (~1 m) satellite imagery. We produce estimates of cropped area for the two countries and sub-national units and our results highlight the significance and complexity of incorporating explicit population characteristics into models of cropped area.
Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique. [Int. J. Climatol. (2009)]