Spatial analysis of the error in a model of soil nitrogen

Models of the dynamics of nitrogen in soil (soil-N) can be used to aid the fertilizer management of a crop. The predictions of soil-N models can be validated by comparison with observed data. Validation generally involves calculating non-spatial statistics of the observations and predictions, such as their means, their mean squared-difference, and their correlation. However, when the model predictions are spatially distributed across a landscape the model requires validation with spatial statistics. There are three reasons for this: (i) the model may be more or less successful at reproducing the variance of the observations at different spatial scales; (ii) the correlation of the predictions with the observations may be different at different spatial scales; (iii) the spatial pattern of model error may be informative. In this study we used a model, parameterized with spatially variable input information about the soil, to predict the mineral-N content of soil in an arable field, and compared the results with observed data. We validated the performance of the N model spatially with a linear mixed model of the observations and model predictions, estimated by residual maximum likelihood. This novel approach allowed us to describe the joint variation of the observations and predictions as: (i) independent random variation that occurred at a fine spatial scale; (ii) correlated random variation that occurred at a coarse spatial scale; (iii) systematic variation associated with a spatial trend. The linear mixed model revealed that, in general, the performance of the N model changed depending on the spatial scale of interest. At the scales associated with random variation, the N model underestimated the variance of the observations, and the predictions were correlated poorly with the observations. At the scale of the trend, the predictions and observations shared a common surface. The spatial pattern of the error of the N model suggested that the observations were affected by the local soil condition, but this was not accounted for by the N model. In summary, the N model would be well-suited to field-scale management of soil nitrogen, but suited poorly to management at finer spatial scales. This information was not apparent with a non-spatial validation. (c),2007 Elsevier B.V. All rights reserved.

A sensitivity analysis of soil moisture retrieval from the t-w microwave emission model

The potential of the τ-ω model for retrieving the volumetric moisture content of bare and vegetated soil from dual polarisation passive microwave data acquired at single and multiple angles is tested. Measurement error and several additional sources of uncertainty will affect the theoretical retrieval accuracy. These include uncertainty in the soil temperature, the vegetation structure and consequently its microwave singlescattering albedo, and uncertainty in soil microwave emissivity based on its roughness. To test the effects of these uncertainties for simple homogeneous scenes, we attempt to retrieve soil moisture from a number of simulated microwave brightness temperature datasets generated using the τ-ω model. The uncertainties for each influence are estimated and applied to curves generated for typical scenarios, and an inverse model used to retrieve the soil moisture content, vegetation optical depth and soil temperature. The effect of each influence on the theoretical soil moisture retrieval limit is explored, the likelihood of each sensor configuration meeting user requirements is assessed, and the most effective means of improving moisture retrieval indicated.

A 3D stochastic cloud model for investigating the radiative properties of inhomogeneous cirrus clouds

(From author). Comments: First 3D stochastic/fractal model of cirrus; first detailed analysis & explanation of power spectra of ice water content, including first observations of 50-km scale break and mixing-induced steepening of spectrum; first demonstration of the potential effect of wind shear on radiative fluxes by changing fall-streak orientation. Has spawned work on the effect of 3D photon transport on the radiative effects of cirrus clouds.

Understanding projections of sea level rise in a Hadley Centre coupled climate model

Sea level changes resulting from CO2-induced climate changes in ocean density and circulation have been investigated in a series of idealised experiments with the Hadley Centre HadCM3 AOGCM. Changes in the mass of the ocean were not included. In the global mean, salinity changes have a negligible effect compared with the thermal expansion of the ocean. Regionally, sea level changes are projected to deviate greatly from the global mean (standard deviation is 40% of the mean). Changes in surface fluxes of heat, freshwater and wind stress are all found to produce significant and distinct regional sea level changes, wind stress changes being the most important and the cause of several pronounced local features, while heat and freshwater flux changes affect large parts of the North Atlantic and Southern Ocean. Regional change is related mainly to density changes, with a relatively small contribution in mid and high latitudes from change in the barotropic circulation. Regional density change has an important contribution from redistribution of ocean heat content. In general, unlike in the global mean, the regional pattern of sea level change due to density change appears to be influenced almost as much by salinity changes as by temperature changes, often in opposition. Such compensation is particularly marked in the North Atlantic, where it is consistent with recent observed changes. We suggest that density compensation is not a property of climate change specifically, but a general behavior of the ocean.

Krakatoa lives: The effect of volcanic eruptions on ocean heat content and thermal expansion

A suite of climate model experiments indicates that 20th Century increases in ocean heat content and sea-level ( via thermal expansion) were substantially reduced by the 1883 eruption of Krakatoa. The volcanically-induced cooling of the ocean surface is subducted into deeper ocean layers, where it persists for decades. Temporary reductions in ocean heat content associated with the comparable eruptions of El Chichon ( 1982) and Pinatubo ( 1991) were much shorter lived because they occurred relative to a non-stationary background of large, anthropogenically-forced ocean warming. Our results suggest that inclusion of the effects of Krakatoa ( and perhaps even earlier eruptions) is important for reliable simulation of 20th century ocean heat uptake and thermal expansion. Inter-model differences in the oceanic thermal response to Krakatoa are large and arise from differences in external forcing, model physics, and experimental design. Systematic experimentation is required to quantify the relative importance of these factors. The next generation of historical forcing experiments may require more careful treatment of pre-industrial volcanic aerosol loadings.

Global climate and ocean circulation on an aquaplanet ocean-atmosphere general circulation model

A low resolution coupled ocean-atmosphere general circulation model OAGCM is used to study the characteristics of the large scale ocean circulation and its climatic impacts in a series of global coupled aquaplanet experiments. Three configurations, designed to produce fundamentally different ocean circulation regimes, are considered. The first has no obstruction to zonal flow, the second contains a low barrier that blocks zonal flow in the ocean at all latitudes, creating a single enclosed basin, whilst the third contains a gap in the barrier to allow circumglobal flow at high southern latitudes. Warm greenhouse climates with a global average air surface temperature of around 27C result in all cases. Equator to pole temperature gradients are shallower than that of a current climate simulation. Whilst changes in the land configuration cause regional changes in temperature, winds and rainfall, heat transports within the system are little affected. Inhibition of all ocean transport on the aquaplanet leads to a reduction in global mean surface temperature of 8C, along with a sharpening of the meridional temperature gradient. This results from a reduction in global atmospheric water vapour content and an increase in tropical albedo, both of which act to reduce global surface temperatures. Fitting a simple radiative model to the atmospheric characteristics of the OAGCM solutions suggests that a simpler atmosphere model, with radiative parameters chosen a priori based on the changing surface configuration, would have produced qualitatively different results. This implies that studies with reduced complexity atmospheres need to be guided by more complex OAGCM results on a case by case basis.

Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid.

Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.

Farming systems methodology for efficient resource management at the farm level: a review from an Indian perspective

Farming systems research is a multi-disciplinary holistic approach to solve the problems of small farms. Small and marginal farmers are the core of the Indian rural economy Constituting 0.80 of the total farming community but possessing only 0.36 of the total operational land. The declining trend of per capita land availability poses a serious challenge to the sustainability and profitability of farming. Under such conditions, it is appropriate to integrate land-based enterprises such as dairy, fishery, poultry, duckery, apiary, field and horticultural cropping within the farm, with the objective of generating adequate income and employment for these small and marginal farmers Under a set of farm constraints and varying levels of resource availability and Opportunity. The integration of different farm enterprises can be achieved with the help of a linear programming model. For the current review, integrated farming systems models were developed, by Way Of illustration, for the marginal, small, medium and large farms of eastern India using linear programming. Risk analyses were carried out for different levels of income and enterprise combinations. The fishery enterprise was shown to be less risk-prone whereas the crop enterprise involved greater risk. In general, the degree of risk increased with the increasing level of income. With increase in farm income and risk level, the resource use efficiency increased. Medium and large farms proved to be more profitable than small and marginal farms with higher level of resource use efficiency and return per Indian rupee (Rs) invested. Among the different enterprises of integrated farming systems, a chain of interaction and resource flow was observed. In order to make fanning profitable and improve resource use efficiency at the farm level, the synergy among interacting components of farming systems should be exploited. In the process of technology generation, transfer and other developmental efforts at the farm level (contrary to the discipline and commodity-based approaches which have a tendency to be piecemeal and in isolation), it is desirable to place a whole-farm scenario before the farmers to enhance their farm income, thereby motivating them towards more efficient and sustainable fanning.

Comparison of energy evaluation systems and a mechanistic model for milk production by dairy cattle offered fresh grass-based diets

Grass-based diets are of increasing social-economic importance in dairy cattle farming, but their low supply of glucogenic nutrients may limit the production of milk. Current evaluation systems that assess the energy supply and requirements are based on metabolisable energy (ME) or net energy (NE). These systems do not consider the characteristics of the energy delivering nutrients. In contrast, mechanistic models take into account the site of digestion, the type of nutrient absorbed and the type of nutrient required for production of milk constituents, and may therefore give a better prediction of supply and requirement of nutrients. The objective of the present study is to compare the ability of three energy evaluation systems, viz. the Dutch NE system, the agricultural and food research council (AFRC) ME system, and the feed into milk (FIM) ME system, and of a mechanistic model based on Dijkstra et al. [Simulation of digestion in cattle fed sugar cane: prediction of nutrient supply for milk production with locally available supplements. J. Agric. Sci., Cambridge 127, 247-60] and Mills et al. [A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation and application. J. Anim. Sci. 79, 1584-97] to predict the feed value of grass-based diets for milk production. The dataset for evaluation consists of 41 treatments of grass-based diets (at least 0.75 g ryegrass/g diet on DM basis). For each model, the predicted energy or nutrient supply, based on observed intake, was compared with predicted requirement based on observed performance. Assessment of the error of energy or nutrient supply relative to requirement is made by calculation of mean square prediction error (MSPE) and by concordance correlation coefficient (CCC). All energy evaluation systems predicted energy requirement to be lower (6-11%) than energy supply. The root MSPE (expressed as a proportion of the supply) was lowest for the mechanistic model (0.061), followed by the Dutch NE system (0.082), FIM ME system (0.097) and AFRCME system(0.118). For the energy evaluation systems, the error due to overall bias of prediction dominated the MSPE, whereas for the mechanistic model, proportionally 0.76 of MSPE was due to random variation. CCC analysis confirmed the higher accuracy and precision of the mechanistic model compared with energy evaluation systems. The error of prediction was positively related to grass protein content for the Dutch NE system, and was also positively related to grass DMI level for all models. In conclusion, current energy evaluation systems overestimate energy supply relative to energy requirement on grass-based diets for dairy cattle. The mechanistic model predicted glucogenic nutrients to limit performance of dairy cattle on grass-based diets, and proved to be more accurate and precise than the energy systems. The mechanistic model could be improved by allowing glucose maintenance and utilization requirements parameters to be variable. (C) 2007 Elsevier B.V. All rights reserved.

Building synergies in development research: The application of the mosaic model to animal health research

Development research has responded to a number of charges over the past few decades. For example, when traditional research was accused of being 'top-down', the response was participatory research, linking the 'receptors' to the generators of research. As participatory processes were recognised as producing limited outcomes, the demand-led agenda was born. In response to the alleged failure of research to deliver its products, the 'joined-up' model, which links research with the private sector, has become popular. However, using examples from animal-health research, this article demonstrates that all the aforementioned approaches are seriously limited in their attempts to generate outputs to address the multi-faceted problems facing the poor. The article outlines a new approach to research: the Mosaic Model. By combining different knowledge forms, and focusing on existing gaps, the model aims to bridge basic and applied findings to enhance the efficiency and value of research, past, present, and future.

Seeds of growth? Agricultural productivity and the transitional dynamics of the Ramsey model

A two-sector Ramsey-type model of growth is developed to investigate the relationship between agricultural productivity and economy-wide growth. The framework takes into account the peculiarities of agriculture both in production ( reliance on a fixed natural resource base) and in consumption (life-sustaining role and low income elasticity of food demand). The transitional dynamics of the model establish that when preferences respect Engel's law, the level and growth rate of agricultural productivity influence the speed of capital accumulation. A calibration exercise shows that a small difference in agricultural productivity has drastic implications for the rate and pattern of growth of the economy. Hence, low agricultural productivity can form a bottleneck limiting growth, because high food prices result in a low saving rate.

Modelling simultaneously water content and dry matter dynamics of wheat grains

A model was devised to describe simultaneously the grain masses of water and dry matter against thermal time during grain filling and maturation of winter wheat. The model accounted for a linear increase in water mass of duration anthesis-m(1) (end of rapid water assimilation phase) and rate a, followed by a more stable water mass until in,, after which water mass declined rapidly at rate e. Grain dry matter was described as a linear increase of rate bgf until a maximum size (maxgf) was attained at m(2).The model was fitted to plot data from weekly samples of grains taken from replicated field experiments investigating effects of grain position (apical or medial), fungicide (five contrasting treatments), sowing date (early or late), cultivar (Malacca or Shamrock) and season (2001/2002 and 2002/2003) on grain filling. The model accounted for between 83 and 99% of the variation ( 2) when fitted to data from individual plots, and between 97 and 99% when fitted to treatment means. Endosperm cell number of grains from early-sown plots in the first season were also counted. Differences in maxgf between grain positions and also between cultivars were mostly the result of effects on bgf and were empirically associated with water mass at nil. Fungicide application controlled S. tritici and powdery mildew infection, delayed flag leaf senescence, increased water mass at m(1) (wm(1)), and also increased m(2), bgf and maxgf. Fungicide effects on water mass were detected before fungicide effects on dry matter, but comparison of the effects of individual fungicide treatments showed no evidence that effects on wm(1), nor on endosperm cell numbers at about m(1), were required for fungicide effects on maxgf, (c) 2005 Elsevier B.V. All rights reserved.

Nectar resource diversity organises flower-visitor community structure

Communities of nectar-producing plants show high spatio-temporal variation in the patterns of volume and concentration presentation. We illustrate a novel approach for quantifying nectar reward structures in complex communities, demonstrating that nectar resource diversity (defined as the variety of nectar volume-concentration combinations available) may be a fundamental factor organising nectarivore communities. In a series of diverse bee and entomophilous flower communities in Israel, our measure of nectar resource diversity alone explains the majority of variation in bee species richness, while other nectar variables (volume, concentration, energy value, and water content) have little predictive value per se. The new measure of nectar resource diversity is highly correlated with floral species richness and particularly with the species richness of annuals, yet it is additive in its effect on bee diversity. We conclude that relying solely upon measurements of mean nectar volume and mean nectar concentration overlooks a key characteristic of community-level reward structure, nectar resource diversity, so that previous studies may have failed to identify an important determinant of flower-visitor community structure.