The relationships between crop yields, weather and climate have been the focus of a great deal of attention in the Earth system science literature.This is due to concerns about securing food supplies for our growing populations and the potential challenges that climate change poses.Most studies have been concerned with establishing the current relationships between climate and crop yields, or making projections about changes in crop yields due to future climate change rather than extending this approach back into the past.Where historical information is used, it tends to be on a relatively recent time scale.Recently, researchers have attempted to infer the location and intensity of agricultural production during the Holocene on a global scale.These estimates are ultimately derived from estimates of past population sizes and make assumptions about how human populations use land for agriculture.Although such studies should be applauded for their ambitious scale, they have a number of features that make them less-than-ideal for our purposes.First, in order to test certain theories it is desirable to separate out achieved production and population from potential production and population.A number of interesting hypotheses about human social and political evolution invoke “population pressure” as a key variable in causing changes in human societies.For example,grow table hydroponic demographic-structural theory , argues that state instability and societal collapse is a result of the pressures on resources from population growth, which, in turn, leads to population decline.
Boserupian models of agricultural change, mentioned above, see agricultural innovations themselves as resulting from population pressure.Second, this approach does not make full use of the historical and archaeological information about past agricultural systems that could potentially inform estimates of productivity.Finally, the data on past population are fairly rough estimates, and are typically made at the coarsegrain level of a province or whole country.There is always some degree of uncertainty associated with these estimates, and unless handled with care, such an approach can indicate a false level of precision, given the data that are being used as inputs.In order to understand the impact of agriculture and increasing productivity on human societies, we need a “bottom-up” approach that estimates productivity or potential productivity independently of population size.Of key theoretical interest is using this information to estimate the carrying capacity of a given region.For our purposes, we define carrying capacity as the maximum human population size that can be supported in a given unit of space.It is a function of the physical and biological characteristics of the region being examined and is also dependent on the types of agricultural technology and techniques possessed by the population that affect the productivity of the crops grown in that region.Carrying capacity is something that can be calculated across agricultural systems and, therefore, facilitates comparisons between different time periods and regions.Furthermore, it is an important variable because it enables us to compare the actual population to the size of the population that could possibly inhabit such a region, including cases where there is a substantial mismatch between these two estimates.This can provide a measure of the population pressure a society experiences.
Mismatches could also reflect cases where a surplus is produced in order to guard against shortfalls in some years or where a substantial proportion of productivity is diverted to elite members of society.In the former case, we would expect actual population and a measure of carrying capacity that took into account annual fluctuations to converge over longer time periods, whereas this would not be the case in the latter example.The measure of carrying capacity can include technological or other cultural features that affect crop productivity.Therefore, over suitably long time periods and geographic scales, this estimate of carrying capacity will also provide a measure of relative agricultural productivity.In other words, in the absence of direct assessments of actual productivity, this measure is still likely to be informative about which regions and time periods were more productive than others.Such a measure is also extremely useful for testing many hypotheses about socio-cultural evolution.Previous work has attempted to calculate carrying capacity for hunter gatherers , which is a somewhat more straightforward task than for agriculturalists.This is because foragers’ sources of food are determined primarily by external climatic conditions and other characteristics of the physical environment, such as “unearned” sources of water, including rivers, which enable plant growth in otherwise arid environments.Although such climatic and environmental considerations are obviously important for agriculturalists, calculating agricultural carrying capacity has a number of added complications.One such factor is the characteristics of crops.Hunter-gatherer population densities tend to be highest in tropical regions with high temperatures and greater amounts of rainfall, i.e.where net primary production is high.On the other hand, large agricultural populations can be supported by grain crops derived from wild grasses.
Cereal productivity, and, therefore, agricultural population density, tends to be greatest when annual patterns of rainfall create seasonal climates that allow grains to dry properly , which is generally at higher latitudes.For example, in island Southeast Asia, rice productivity is highest in regions such as Java, where monsoon conditions create a more distinct dry season.Humans are also niche constructors par excellence , and agriculture is probably one of the most dramatic representations of our ability to substantially modify our environment and, thus, reduce or ameliorate the impact of external environmental factors.Artificial selection has also been a key process in improving crops and increasing yields over time, so having information about historic cultivars and varieties is of great importance.In addition to these crop characteristics, another important determinant of agricultural productivity is the level of agricultural technology and the specific agricultural practices that enhance productivity, which have varied dramatically in time and space.We return to this issue below.The fundamental idea behind this approach to estimating carrying capacity is to construct a function that predicts crop productivity based on a variety of theoretically informed inputs, the parameters of which will then be estimated and empirically validated.This estimate in terms of energy can then be converted into a population estimate based on an understanding of the energy requirements of human populations.In both cases, calibration and validation will require historical information about past crop productivities, ideally with as broad a geographic and temporal distribution as possible.Figure 2 shows examples of changing productivities of two cereal crops in two regions in Europe.In both cases, productivity has increased, but to what degree these changes are due to changes in climate, technology, or genetics needs to be assessed.Obviously, estimating potential agricultural productivity on a global scale and over long time periods is not an easy task.In order to make this task manageable, it will be important to employ a number of simplifying assumptions and strategies.
Because we are interested in assessing the amount of energy produced, a reasonable starting point is to focus on the major carbohydrate source grown.For example, based estimates of potential pre-Hispanic productivity in the valley of Oaxaca using only information on a single crop, maize.Previous experience with calculating carrying capacity in Europe suggests that reasonably accurate estimates can be obtained just by using a single crop such as wheat or rye.The focal crop will, of course, vary from region to region due to different histories of domestication and the spread of different crops.In some cases, when different crops seriously affect the estimate, it may be advisable to estimate carrying capacities based on more than one crop.In some places, ecological conditions may vary over a relatively small distance, such that one crop does well where another one does poorly.For example, Pacific islands are characterized by wet conditions on the windward sides,grow table where taro does best, and drier conditions on the leeward side, which favors sweet potato.Agricultural productivity varies in space and, importantly, in time.In recent years, a large amount of work has been conducted on historical climate change and the effects of climate on crop productivity.This work needs supplementing with information about historical crop yields and the cultural and technological factors that affect agricultural productivity.Unfortunately, such data are not readily available in the kind of systematic manner on a global scale that would aid these endeavors due to the general turn away from broad-scale theorizing and comparative perspectives in disciplines such as anthropology, archaeology, and history.Here, we demonstrate how initiative that we have developed, Seshat: The Global History Databank 2, can provide a framework for collecting the necessary information to model agricultural productivity in the past and, more generally, to test comparative hypotheses about cultural evolution and human history.Most historians and archaeologists studying agricultural systems or other aspects of human societies tend to be experts in particular time periods and/or tightly defined regions.Although there are some who argue that there are broadscale patterns and general processes shaping human history, their claims tend to rely on illustrative examples and are not systematically tested in the manner that is common in the natural sciences.However, in order to test competing ideas properly, a more rigorous way of adjudicating between alternative hypotheses is required.A barrier to such an endeavor is the lack of data of suitable quantity and quality in the kind of systematic format that is required.It is for these reasons that the Seshat project aims to work directly with historians and other relevant experts to construct a large-scale database that collates the most up-to-date knowledge and understanding of past human societies in a systematic manner.Importantly, the information is coded into well-defined variables suitable for statistical analyses so that different hypotheses can be rigorously tested.Although the Seshat approach can be applied to any aspect of human societies, in this paper, we focus in on the variables of relevance to agriculture.
As a sampling strategy, we have selected 30 regions of roughly 10,000 square kilometers from around the world that are delimited by natural geographic features, such valleys, plains, mountains, coasts, or islands.Examples of these Natural Geographic Areas, or NGAs, include Latium , Upper Egypt, Hawaii, and the Kansai region of Japan.We have employed a stratified sampling strategy such that the NGAs are broadly distributed geographically and exhibit substantial variation in the polities that inhabited these NGAs in terms of the degree and timing of the appearance of the first large-scale, complex societies.For information related to agricultural systems for each NGA, we are gathering data on variables that relate to the NGA itself and the forms of agriculture practiced there, going back as far as possible in the Holocene.In related projects, we are capturing information about all the polities that occupied the NGA during this time.This will allow us to match different sources of information about different aspects of human societies and enable us to test a range of different hypotheses about human social and cultural evolution.What information do we need to capture about past societies in order to estimate the productivity of agricultural systems? Over the last two years, members of our research team have been developing a codebook to describe the variables relating to agricultural productivity.Typically, variables in the codebook relate to the presence or absence of certain features , naming of specific features that were present , or a quantitative measure of certain features.The development of this codebook has been an iterative process, and has improved through discussing these issues with experts on agriculture in past societies.For each NGA, we examine the variables of interest during the time since agriculture was first practiced until the present day.Research assistants work with expert historians and archaeologists to identify the most relevant literature, attempt to code the variables in the codebook from these sources, and, where possible, indicate the time at which features appear or change.These codings are then ultimately checked for accuracy by experts in the appropriate region and/or time period.Currently, the variables we are coding relate to Land Use, Features of Cultivation, Technology & Practices, Conventions & Techniques, Post-Harvest practices, Food Storage and Preservation, Social Scale of Food Production, Agricultural Intensity, and Major Carbohydrate Sources.We describe each of the categories below and illustrate the kinds of variables we are capturing within them.Land use variables relate to the areas of the NGA that were either used for agriculture or that could potentially be cultivated.To give a couple of modern examples, according to the CIA World Factbook , around 25% of the total area of the United Kingdom is given over to crop production, whereas Japan, with its much more mountainous terrain, devotes only 12% of its land to producing crops.