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Global agriculture is under pressure to meet increasing demand for food and agricultural products. There are several global assessments of crop yields, but we know little about the uncertainties of their key findings, as the assessments are driven by the single best yield dataset available when each assessment was conducted. Recently, two different spatially explicit, global, historical yield datasets, one based on agricultural census and the other largely based on satellite remote sensing, became available. Using these datasets, we compare the similarities and differences in global yield gaps, trend patterns, growth rates and changes in year-to-year variability. We analyzed maize, rice, wheat and soybean for the period of 1981 to 2008 at four resolutions (0.083°, 0.5°, 1.0° and 2.0°). Although estimates varied by dataset and resolution, the global mean annual growth rates of 1.7–1.8%, 1.5–1.7%, 1.1–1.3% and 1.4–1.6% for maize, rice, wheat and soybean, respectively, are not on track to double crop production by 2050. Potential production increases that can be attributed to closing yield gaps estimated from the satellite-based dataset are almost twice those estimated from the census-based dataset. Detected yield variability changes in rice and wheat are sensitive to the choice of dataset and resolution, but they are relatively robust for maize and soybean. Estimates of yield gaps and variability changes are more uncertain than those of yield trend patterns and growth rates. These tendencies are consistent across crops. Efforts to reduce uncertainties are required to gain a better understanding of historical change and crop production potential to better inform agricultural policies and investments. [ABSTRACT FROM AUTHOR]

Consumers, including the poor in many countries, are increasingly dependent on food imports and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop-rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes. [ABSTRACT FROM AUTHOR]

Abstract: Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September–November. In Eastern Africa the start of the June–October season strongly correlates with PDO in March–May, while the PDO in December–February correlates with the start of the February–June season. The cumulative NDVI over this last season relates to the MEI of March–May. For Southern Africa, high correlations exist between SOS and NAO of September–November, and cumulative NDVI and MEI of March–May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa. [ABSTRACT FROM AUTHOR]

The article examines the relationship between markets, climate change, and food security in West Africa. The focus of the article is how the global fluctuations of food prices have made food security in West Africa tenuous. The article also discusses the role of technology in preventing food insecurity. According to the author, the use of technology has transformed agriculture and food production through advancements and improvements in irrigation, seed quality, and machinery; which has increased crop yields and the productivity of workers. Topics include an in-depth discussion of the agricultural systems in West Africa, including a welfare analysis of food prices in relation to sellers and buyers, the integration with global markets, and the use of technology to increase production.

NASA provides daily satellite remote sensing observations on a wide variety of environmental parameters at the global scale, including rainfall, temperature, vegetation health, ocean productivity and soil moisture conditions. Many organizations and individuals use this data to better understand the Earth system using models and diagnose the impact of climate on agricultural productivity. Weather related extreme events trigger food insecurity by reducing the supply of food and incomes of households working in the agriculture sector. Models and other ecosystem-based assessments can be used to inform decisions regarding food security and human health outcomes of changes in access to food. In this talk, I provide a description of the various monitoring products that have been developed by the US Agency for International Development and World Food Program's systems to monitor food production in food insecure regions, along with graphics and explanations as to how the products are used. Information on local food market prices and food affordability, nutrition outcomes, school attendance, the use of safety nets and other ancillary data are brought together with remote sensing information to inform food security. Satellite data is increasingly used to assess the amount of food grown globally, particularly in a new system entitled ‘Global Agriculture Geo-monitoring Initiative (GEOGLAM). The system seeks to harmonize production assessments across nations and regions using satellite data to ensure a comprehensive and transparent assessment of how much food will be available for sale in international markets every month. As the climate changes, improved integration of social and economic food security indicators with climate observations from satellites will become increasingly important to ensure food security for all. [ABSTRACT FROM AUTHOR]

In this article we specify a model of millet prices in the three West African countries of Burkina Faso, Mali, and Niger. Using data obtained from USAID’s Famine Early Warning Systems Network (FEWS NET) we present a unique regional millet price forecasting model that takes advantage of the panel nature of our data and accounts for the distance of rural markets from capital cities. Another novel aspect of our analysis is our use of the Normalized Difference Vegetation Index (NDVI) to detect and control for variation in conditions for productivity. We find that including NDVI information significantly improves price forecasts. [ABSTRACT FROM AUTHOR]