Agricultural productivity in past societies: toward an empirically informed model for testing cultural evolutionary hypotheses

Agricultural productivity, and its variation in space and time, plays a fundamental role in many theories of human social evolution. However, we often lack systematic information about the productivity of past agricultural systems on a scale large enough to test these theories properly. The effect of climate on crop yields has received a great deal of attention resulting in a range of empirical and process-based models, yet the focus has primarily been on current or future conditions. In this paper, we argue for a “bottom-up” approach that estimates potential productivity based on information about the agricultural practices and technologies used in past societies. Of key theoretical interest is using this information to estimate the carrying high quality historical and archaeological information about past societies in order to infer the temporal and geographic patterns of change in agricultural productivity and potential. We discuss information we need to collect about past agricultural techniques and practices, and introduce a new databank initiative that we have developed for collating the best available historical and archaeological evidence. A key benefit of our approach lies in making explicit the steps in the estimation of past productivities and carrying capacities, and in being able to assess the effects of different modelling assumptions. This is undoubtedly an ambitious task, yet promises to provide important insights into fundamental aspects of past societies, enabling us to test more rigorously key hypotheses about human socio-cultural evolution

Emulating global climate change impacts on crop yields

The potential effects of climate change on the environment and society are many. In order to effectively quantify the uncertainty associated with these effects, highly complex simulation models are run with detailed representations of ecosystem processes. These models are computationally expensive and can involve a computer run of several days. Computationally cheaper models can be obtained from large ensembles of simulations using statistical emulation. The purpose of this paper is to construct a cheaper computational model (emulator) from simulations of the Lund- Potsdam-Jena managed Land (LPJmL), which is a dynamic global vegetation and crop model. This paper focuses on statistical emulation of potential crop yields from LPJmL and an emulator is constructed using a combination of ordinary least squares, principal component analysis and weighted least squares methods. For five climate models, under cross-validation the percentage of variance explained ranges from 60- 88% for the rainfed crops and 62-93% for the irrigated crops. The emulator can be used to predict potential crop yield change under any future climate scenarios and management options

A Bayesian approach to modelling the impact of hydrodynamic shear stress on biofilm deformation

We investigate the feasibility of using a surrogate-based method to emulate the deformation and detachment behaviour of a biofilm in response to hydrodynamic shear stress. The influence of shear force, growth rate and viscoelastic parameters on the patterns of growth, structure and resulting shape of microbial biofilms was examined. We develop a statistical modelling approach to this problem, using combination of Bayesian Poisson regression and dynamic linear models for the emulation. We observe that the hydrodynamic shear force affects biofilm deformation in line with some literature. Sensitivity results also showed that the expected number of shear events, shear flow, yield coefficient for heterotrophic bacteria and extracellular polymeric substance (EPS) stiffness per unit EPS mass are the four principal mechanisms governing the bacteria detachment in this study. The sensitivity of the model parameters is temporally dynamic, emphasising the significance of conducting the sensitivity analysis across multiple time points. The surrogate models are shown to perform well, and produced ≈ 480 fold increase in computational efficiency. We conclude that a surrogate-based approach is effective, and resulting biofilm structure is determined primarily by a balance between bacteria growth, viscoelastic parameters and applied shear stress

Density and time series plots for expected number of shear events and volume of detached clusters for different shear rates.

<p>The error bars show ±1 standard deviation calculated from five replicates. Note: volumes are normalized by their initial biofilm volume.</p

IB model parameters.

<p>IB model parameters.</p

Diagnostics plots showing the convergence of the three randomly chosen regression parameters <i>θ</i> of the Bayesian dynamic linear model at time 10000s.

<p>The first column shows the posterior density of the state variables. The middle column is the running ergodic means of MCMC samples. The third colum is the trace plots for the MCMC samples.</p

Diagnostic plots showing the convergence of some of the estimated <i>θ</i> parameters of Bayesian Poisson model for expected number of events.

<p>The first column shows the posterior density of the state variables. The middle column is the running ergodic means of MCMC samples. The third colum is the trace plots for the MCMC samples.</p

Barplots showing the sensitivity indices of the volume of detached clusters for the eight input variables over time.

<p>Barplots showing the sensitivity indices of the volume of detached clusters for the eight input variables over time.</p

Biofilm structures showing temporal evolution of effect of shear rate on biofilm deformation and bacteria detachment for different shear rates for the period the flow is applied.

<p>Left-plot: <i>γ</i> = 0.26<i>s</i>⁻¹ and right-plot: <i>γ</i> = 0.37<i>s</i>⁻¹ for 40,000, 80,000, 120,000, 160,000 and 200,000 seconds respectively.</p

Agricultural productivity in past societies: Toward an empirically informed model for testing cultural evolutionary hypotheses

Agricultural productivity, and its variation in space and time, plays a fundamental role in many theories of human social evolution. However, we often lack systematic information about the productivity of past agricultural systems on a large enough scale in order to be able to test these theories properly. The effect of climate on crop yields has received a great deal of attention resulting in a range of empirical and process-based models, yet the focus has primarily been on current or future conditions. In this paper, we argue for a “bottom-up” approach that estimates productivity, or potential productivity based on information about the agricultural practices and technologies used in past societies. Of key theoretical interest is using this information to estimate the carrying capacity of a given region, independently of estimates of population size. We outline how explicit crop yield models can be combined with high quality historical and archaeological information about past societies, in order to infer the temporal and geographic patterns of change in agricultural productivity and potential. We discuss the kinds of information we need to collect about agricultural techniques and practices in the past, and introduce a new databank initiative we have developed for collating the best available historical and archaeological evidence. A key benefit of our approach lies in making explicit the steps in the process of estimating past productivities and carrying capacities, and in being able to assess the effects of different modelling assumptions. This is undoubtedly an ambitious task, yet promises to provide important insights into fundamental aspects of past societies, and will enable us to test more rigorously key hypotheses about human socio-cultural evolution