P.K. Thornton

Mapping livestock-oriented agricultural production systems for the developing world

Abstract Questions as to whether public investment in international agricultural research is a ‘Good Thing’ or not may best be addressed using two arguments: (1) justifications based on whether or not past investments have yielded substantial benefits to societies and the resource-poor; and (2) that future investments need to be made as effectively and efficiently as possible, which means they must be targeted as closely as possible. A major component of any impact assessment framework that aspires to comprehensiveness is information on the location of different agricultural systems and pertinent characteristics of the resource-poor who operate them. Given the importance of livestock to the diets and incomes of poor farming households, and the predicted increase in demand for livestock products throughout the developing world over the next few decades, understanding how livestock fit into these systems, and how these systems may evolve in the future, is critical. This is especially true in Africa, where approximately 27% (162 million people) of the worlds poor livestock keepers live. In this paper, we further develop a global livestock production system classification put forward by Sere and Steinfeld in 1996. These livestock systems fall into four categories: landless systems, livestock only/rangeland-based systems (areas with minimal cropping), mixed rainfed systems (mostly rainfed cropping combined with livestock) and mixed irrigated systems (a significant proportion of cropping uses irrigation and is interspersed with livestock). We then describe a method for mapping the classification, based on agro-climatology (length of growing period), land cover, and human population density. We conclude with a discussion of how the maps could be refined, and indicate their potential use in a range of different policy and research and development applications.

Measuring the potential impacts of improved food-feed crops: methods for ex ante assessment

Abstract The recent increased emphasis on impact assessment is due in part to the rapidly changing nature of funding for agricultural research and the shifts that have occurred in what is expected of the agricultural research community. The reasons for doing impact assessment are relatively clear: ex post studies can determine the impact of past investment in research on target beneficiaries and are a way to learn some of the lessons of the past. Ex ante studies can provide information to assist in the allocation of scarce research resources to activities that best match donors’ development objectives. In practice, impact assessment is often contentious and almost always difficult, particularly when livestock are involved. In this paper, we outline methods that can be used in ex ante impact assessment, and illustrate some of these in relation to three recent studies on improved food-feed crops in different places: improving the quality of millet and sorghum stover in India, using dual-purpose cowpea in West Africa, and alternatives for utilizing maize stover in the mixed systems of East and Southern Africa. Such impact assessments are neither cheap nor quick, and the methods that are most appropriate in any situation will depend not only on the resources and expertise available but most importantly on the exact nature of the questions being asked and the end-users of the results. Much remains to be done to maximize the utility of such assessments, particularly in the areas of quantitative model development, rapid qualitative method development, more effective integration of biophysical and socio-cultural indicators and approaches, and provision of baseline data against which to measure progress. Research resource allocation may well retain its somewhat haphazard nature in the future, but given the challenges facing agriculture in developing countries, a mechanism for attempting to ensure that research and extension really do contribute to widely held development goals has to be based on more than trial and error.

Effects of Climate, Human Population and Socio-economic Changes on Tsetse-transmitted Trypanosomiasis to 2050

This chapter explores the impacts of climate change, human population growth and expected disease control activities on tsetse distribution and trypanosomiasis risk in five agro-ecological environments in sub-Saharan Africa to 2050. These changes will tend to contract areas under trypanosomiasis risk continent-wide; however, this trend will not be uniform. The greatest decrease in the impacts of animal trypanosomiasis will occur in the semi-arid and sub-humid zones of West Africa, where the climate will be drier, human population will increase and disease control will have greater impacts. The risk of animal trypanosomiasis will also decline in many but not all areas of Ethiopia and eastern and southern Africa. The disease situation in the humid zone of central and western Africa will be less changed. Sleeping sickness, particularly the gambiense type, will continue, as now, to be a major problem, if concerted control efforts are not implemented.

The evolution and evaluation of dairy cattle models for predicting milk production: an agricultural model intercomparison and improvement project (AgMIP) for livestock

The contemporary concern about anthropogenic release of greenhouse gas (GHG) into the environment and the contribution of livestock to this phenomenon have sparked animal scientists’ interest in predicting methane (CH4) emissions by ruminants. We contend that improving the adequacy of mathematical nutrition model estimates of production of meat and milk is a sine qua non condition to reliably determine ruminants’ worldwide contribution to GHG. Focusing on milk production, we address six basic nutrition models or feeding standards (mostly empirical systems) and five complex nutrition models (mostly mechanistic systems), describe their key characteristics, and highlight their similarities and differences. We also present derivative systems. We compiled a database of milk production information from 37 published studies from six regions of the world, totalling 173 data points: 19 for Africa, 45 for Asia, 16 for Europe, 12 for Latin America, 44 for North America and 37 for Oceania. Four models were used to predict milk production in lactating dairy cows, and the adequacy of their predictions was measured against the observed milk production from our database. Even though these mathematical nutrition models shared similar assumptions and calculations, they have different conceptual and structural foundations inherent to their intended purposes. A direct comparison among these models was further complicated by the different models requiring unique inputs that are very often not available, and the low reliability of the inputs prevents an unbiased assessment of the model predictions. Very few studies have collected the necessary information to run more mechanistic systems, and users have to rely on standard information to populate many model inputs. Study effect was a critical source of variation that limited our ability to conclusively evaluate the models’ applicability under different scenarios of production around the world. Only after study variation was removed from the database did the adequacy of the model predictions of milk production improved, but deficiencies still existed. On the basis of these analyses, we conclude that not all models were suitable for predicting milk production and that simpler systems might be more resilient to variations in studies and production conditions around the world. Improving the predictability of milk production by mathematical nutrition models is a prerequisite to further development of systems that can effectively and correctly estimate the contribution of ruminants to GHG emissions and their true share of the global warming event.

Markets and climate are driving rapid change in farming practices in Savannah West Africa

Agricultural practices have constantly changed in West Africa, and understanding the factors that have driven the changes may help guide strategies to promote sustainable agriculture in the region. To contribute to such efforts, this paper analyzes drivers of change in farming practices in the region using data obtained from surveys of 700 farming households in five countries (Burkina Faso, Ghana, Mali, Niger and Senegal). The results showed that farmers have adopted various practices in response to the challenges they have faced during the last decade. A series of logit models showed that most changes farmers made to their practices are undertaken for multiple reasons. Land use and management changes including expanding farmed areas and using mineral fertilization and manure are positively related to perceived changes in the climate, such as more erratic rainfall. Planting new varieties, introducing new crops, crop rotation, expanding farmed area and using pesticides are positively associated with new market opportunities. Farm practices that require relatively high financial investment such as use of pesticides, drought-tolerant varieties and improved seeds were positively associated with the provision of technical and financial support for farmers through development projects and policies. Changes in markets and climate are both helping to promote needed changes in farming practices in West Africa. Therefore, policies that foster the development of markets for agricultural products, and improved weather- and climate-related information linked to knowledge of appropriate agricultural innovations in different environments are needed.