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This paper makes the case for innovative risk management approaches in pastoral settings, which may include adjustments to the traditionally practiced approaches that have become progressively less effective. We use recent data from studies in Kenya and southern Ethiopia to confirm that traditional pastoral risk management approaches are increasingly futile against increasing external pressures, seasonal rainfall variability and future climate change. Some pioneering approaches and ideas, with potentially wider application to African pastoral settings, appear to offer greater hope; these include pilot studies designed to demonstrate the efficacy of index-based risk transfer products in pastoral systems, improvements in the management of food insecurity response for pastoralists and the recasting of development interventions as risk management. The International Livestock Research Institute, in collaboration with a wide range of partners, is currently testing these ideas. [ABSTRACT FROM PUBLISHER]

Extension agrometeorology is applied in agrometeorological extension work to advice and serve farmers. In agrometeorology, response farming has been developed decades ago. Climate change complicates response farming, but does not alter it. This paper reports on new operationalization of that response farming in new educational commitments in agroclimatology. It is explained how "Science Field Shops" are an example in Indonesia. This was based on a thorough analysis of what climate change means for farmers in Asia. For Africa, we report on eying the training of agrometeorological extension trainers ("product intermediaries") in West Africa, based on a thorough analysis of what climate change means for farmers in Africa. We also compare experience with reaching farmers in South Africa and farmer communities in Zambia, as new forms of supporting response farming, all under conditions of a changing climate. The paper, for the first time, connects results from four different programs the senior author is taking part in. There is first and foremost the need for training material to make it possible for the product intermediaries to participate in training extension intermediaries. This should, particularly, bring new knowledge to farmers. With what is presently available and with new approaches, climate extension should be developed and tested with farmers in ways that improve farmer preparedness and decision making. [ABSTRACT FROM AUTHOR]

Building resilience to climate change in agricultural production can ensure the functioning of agricultural-based livelihoods and reduce their vulnerability to climate change impacts. This paper thus explores how buffer capacity, a characteristic feature of resilience, can be conceptualised and used for assessing the resilience of smallholder agriculture to climate change. It uses the case of conservation agriculture farmers in a Kenyan region and examines how their practices contribute to buffer capacity. Surveys were used to collect data from 41 purposely selected conservation agriculture farmers in the Laikipia region of Kenya. Besides descriptive statistics, factor analysis was used to identify the key dimensions that characterise buffer capacity in the study context. The cluster of practices characterising buffer capacity in conservation agriculture include soil protection, adapted crops, intensification/irrigation, mechanisation and livelihood diversification. Various conservation practices increase buffer capacity, evaluated by farmers in economic, social, ecological and other dimensions. Through conservation agriculture, most farmers improved their productivity and incomes despite drought, improved their environment and social relations. Better-off farmers also reduced their need for labour, but this resulted in lesser income-earning opportunities for the poorer farmers, thus reducing the buffer capacity and resilience of the latter. [ABSTRACT FROM AUTHOR]

This paper seeks to establish the concept that the analysis of high temporal resolution meteorological data adds value to the investigation of the effect of climatic variability on the prevalence and severity of agricultural pests and diseases. Specifically we attempt to improve disease potential maps of root rots in common beans, based on a combination of inherent susceptibility and the risk of exposure to critical weather events. We achieve this using simulated datasets of daily rainfall to assess the probability of heavy rainfall events at particular times during the cropping season. We then validate these simulated events with observations from meteorological stations in East Africa. We also assess the utility of remotely sensed daily rainfall estimates in near real time for the purposes of updating the risks of these events over large areas and for providing warnings of potential disease outbreaks. We find that simulated rainfall data provide the means to assess risk over large areas, but there are too few datasets of observed rainfall to definitively validate the probabilities of heavy rainfall events generated using rainfall simulations such as those generated by MarkSim. We also find that selected satellite rainfall estimates are unable to predict observed rainfall events with any power, but data from a sufficiently dense network of rain gauges are not available in the region. Despite these problems we show that remotely sensed rainfall estimates may provide a more realistic assessment of rainfall over large areas where rainfall observations are not available, and alternative satellite estimates should be explored. [ABSTRACT FROM PUBLISHER]

Climate-related risks to African agriculture are highly contextual. Climatic conditions are changing in diverse agro-ecological environments throughout Africa, and populations are being affected by, and responding to, these changes. The paper describes how climate change risks in African agriculture are mediated by multiple factors, ranging from the availability of physical resources through policy contexts to the role of culture. Consequently, support to adaptation needs to be complemented with research that can generate contextual information to inform adaptation policies, strategies, and measures. Interventions need to go beyond just technical fixes such as the development of new crop varieties, and must be based on an understanding of how different factors interact in a complex manner to drive risks and results in specific contexts to diverse outcomes. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to present a new data set of bias-corrected CMIP5 global climate models (GCMs) daily data over Africa. This dataset was obtained in using the Cumulative Distribution Function Transform (CDF-t) method, a method that has been applied on several regions and contexts but never on Africa. Here CDF-t is used over the period 1950–2099 combining historical runs and climate change scenarios on 6 variables, precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface down-welling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. Evaluation of the results is carried out over West Africa on a list of priority users-based metrics that was discussed and selected with stakeholders and on simulated yield using a crop model simulating maize growth. Bias-corrected GCMs data are compared with another available dataset of bias-corrected GCMs, and the impact of three different reference datasets on bias-corrections is also examined in details. CDF-t is very effective in removing the biases and in reducing the high inter-GCMs scattering. Differences with other bias-corrected GCMs data are mainly due to the differences between the reference datasets. This is particular true for surface down-welling shortwave radiation, which has impacts in terms of simulated maize yields. Projections of future yields over West Africa have quite different levels, depending on bias-correction method used, but they all show a similar relative decreasing trend over the 21st century. [ABSTRACT FROM AUTHOR]

Africa is one of the regions with high mycotoxin contamination of foods and continues to record high incidences of liver cancers globally. The agricultural sector of most African countries depends largely on climate variables for crop production. Production of mycotoxins is climate-sensitive. Most stakeholders in the food production chain in Africa are not aware of the health and economic effects of consuming contaminated foods. The aim of this review is to evaluate the main factors and their degree of contribution to the high levels of mycotoxins in African foods. Thus, knowledge of the contributions of different factors responsible for high levels of these toxins will be a good starting point for the effective mitigation of mycotoxins in Africa. Google Scholar was used to conduct a systemic search. Six factors were found to be linked to high levels of mycotoxins in African foods, in varying degrees. Climate change remains the main driving factor in the production of mycotoxins. The other factors are partly man-made and can be manipulated to become a more profitable or less climate-sensitive response. Awareness of the existence of these mycotoxins and their economic as well as health consequences remains paramount. The degree of management of these factors regarding mycotoxins varies from one region of the world to another. [ABSTRACT FROM AUTHOR]

Earth observation (EO) data are increasingly being used to monitor vegetation and detect plant growth anomalies due to water stress, drought, or pests, as well as to monitor water availability, weather conditions, disaster risks, land use/land cover changes and to evaluate soil degradation. Satellite data are provided regularly by worldwide organizations, covering a wide variety of spatial, temporal and spectral characteristics. In addition, weather, climate and crop growth models provide early estimates of the expected weather and climatic patterns and yield, which can be improved by fusion with EO data. The AfriCultuReS project is capitalizing on the above to contribute towards an integrated agricultural monitoring and early warning system for Africa, supporting decision‐making in the field of food security. The aim of this article is to present the design of EO services within the project, and how they will support food security in Africa. The services designed cover the users' requirements related to climate, drought, land, livestock, crops, water, and weather. For each category of services, results from one case study are presented. The services will be distributed to the stakeholders and are expected to provide a continuous monitoring framework for early and accurate assessment of factors affecting food security in Africa. [ABSTRACT FROM AUTHOR]

In semi-arid Africa, rainfall variability is an important issue for ecosystems and agricultural activities. However, due to its discrete nature in time and space, rainfall is difficult to measure, quantify, and predict. In the dry tropics, a good proxy for rainfall is vegetation activity since this parameter is well correlated with rainfall variations. In this study, over 20 years of Normalized Difference Vegetative Index (NDVI) data from the Advanced Very High Resolution Radiometers are used. The goal is to assess the skill of linear statistical models in estimating regional NDVI interannual variability based on ocean and atmospheric fields (but not rainfall) and then to hindcast it with a 1- to 2-month lead-time. Three semi-arid areas of ∼150 000 km2 located in Western, Southern, and Eastern Tropical Africa are considered for this purpose. The predictors are: the Niño3.4 sea surface temperature index, the main modes of National Center for Environmental Prediction (NCEP) surface temperature variability in a window centered over Africa, and regional-scale indices based on NCEP surface temperatures and atmospheric variables (relative humidity, geopotential heights, and winds). The regional indices, which are physically and statistically robust, are generally asynchronous with the NDVI predictand. The statistical models, based on linear multiple regressions, give significant results, and the correlation between observed and cross-validated NDVI is 0.67 in Southern Africa, 0.76 for the long rains and 0.83 for the short rains in Eastern Africa, and 0.88 in Western Africa. The results have implications for (1) better understanding the role of El Niño/Southern Oscillation in semi-arid Africa, and (2) highlight the importance of regional climate processes for vegetation growth at these scales, notably the role played by the Mediterranean Sea and its influence on the West African monsoon. The predictability of NDVI over these African regions is discussed. [ABSTRACT FROM AUTHOR]

We are comparing spatially explicit processmodel based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJDGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are partly corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa. [ABSTRACT FROM AUTHOR]

The article reports on the study of agro-ecosystems in sub-Saharan Africa and how rainfall variability and poor soils result in land degradation and how those factors change these systems. The area studied is the Makanya catchment in Tanzania, and the drivers considered are the methods of natural resource management, the dry spell rain frequency, and high population growth. The combination of these seems to have caused the degradation, and the study suggests how the local population can reverse these land degradation changes.

During the last two decades, several land surface schemes for use in climate, regional and/or mesoscale, hydrological and ecological models have been designed. Incorrect parametrization of land-surface processes and prescription of the surface parameters in atmospheric modeling, can result in artificial changes of the horizontal gradient of the sensible heat flux. Thus, an error in horizontal temperature gradient within the lower atmosphere may be introduced. The reliability of the model depends on the quality of boundary layer scheme implemented and its sensitivity to the bare soil and vegetation parameters. In this study, a series of sensitivity experiments has been conducted over broad time scales, using a version of the ARPEGE Climate Model coupled to the ISBA land surface scheme in order to investigate model sensitivity to separate changes in land surface parameters over Africa. Effects of perturbing vegetation cover, distribution of soil depth, albedo of vegetation, roughness length, leaf area index and minimum stomatal resistance were explored by using a simple statistical analysis. Identifying which parameters are important in controlling turbulent energy fluxes, temperature and soil moisture is dependent on which variables are used to determine sensibility, which type of vegetation and climate regime is being simulated and the magnitude and sign of the parameter change. This study does not argue that a particular parameter is important in ISBA, rather it shows that no general ranking of parameters is possible. So, it is essential to specify all land surface parameters with greater precision when attempting to determine the climate response to modification of the land surface. The implication of ISBA being sensitive to parameters that cannot be validated suggests that there will always be considerable doubt over the predictive quality of land-surface schemes. [ABSTRACT FROM AUTHOR]

Studies a model for the growth and development of cassava, Manihot esculenta, in Africa, as modified by weather, soil nitrogen and water. Use of the model to evaluate damage by exotic cassava pests and the effectiveness of control by natural enemies; Relationships between trophic level models at the population and per capita levels; Modification of the Frazer-Gilbert functional response model from animal ecology.