SummaryBackgroundInformation about the global structure of agriculture and nutrient production and its diversity is essential to improve present understanding of national food production patterns, agricultural livelihoods, and food chains, and their linkages to land use and their associated ecosystems services. Here we provide a plausible breakdown of global agricultural and nutrient production by farm size, and also study the associations between farm size, agricultural diversity, and nutrient production. This analysis is crucial to design interventions that might be appropriately targeted to promote healthy diets and ecosystems in the face of population growth, urbanisation, and climate change.MethodsWe used existing spatially-explicit global datasets to estimate the production levels of 41 major crops, seven livestock, and 14 aquaculture and fish products. From overall production estimates, we estimated the production of vitamin A, vitamin B12, folate, iron, zinc, calcium, calories, and protein. We also estimated the relative contribution of farms of different sizes to the production of different agricultural commodities and associated nutrients, as well as how the diversity of food production based on the number of different products grown per geographic pixel and distribution of products within this pixel (Shannon diversity index [H]) changes with different farm sizes.FindingsGlobally, small and medium farms (≤50 ha) produce 51–77% of nearly all commodities and nutrients examined here. However, important regional differences exist. Large farms (>50 ha) dominate production in North America, South America, and Australia and New Zealand. In these regions, large farms contribute between 75% and 100% of all cereal, livestock, and fruit production, and the pattern is similar for other commodity groups. By contrast, small farms (≤20 ha) produce more than 75% of most food commodities in sub-Saharan Africa, southeast Asia, south Asia, and China. In Europe, west Asia and north Africa, and central America, medium-size farms (20–50 ha) also contribute substantially to the production of most food commodities. Very small farms (≤2 ha) are important and have local significance in sub-Saharan Africa, southeast Asia, and south Asia, where they contribute to about 30% of most food commodities. The majority of vegetables (81%), roots and tubers (72%), pulses (67%), fruits (66%), fish and livestock products (60%), and cereals (56%) are produced in diverse landscapes (H>1·5). Similarly, the majority of global micronutrients (53–81%) and protein (57%) are also produced in more diverse agricultural landscapes (H>1·5). By contrast, the majority of sugar (73%) and oil crops (57%) are produced in less diverse ones (H≤1·5), which also account for the majority of global calorie production (56%). The diversity of agricultural and nutrient production diminishes as farm size increases. However, areas of the world with higher agricultural diversity produce more nutrients, irrespective of farm size.InterpretationOur results show that farm s...
In this paper we demonstrate how satellite images and other geographic data can be used to predict land use. A cross-section model of land use is estimated with data for a region in central Mexico. Parameters from the model are used to examine the effects of reduced human activity. If variables that proxy human influence are changed to reflect reduced impact, “forest” area increases and “irrigated crop” area is reduced. Copyright 1997, Oxford University Press.
Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of "real world commodity prices" differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between −0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro-economic modelers aiming to inform the agricultural and development policy debate require
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