A New Soil Conservation Methodology and Application to Cropping Systems in Tropical Steeplands: A comparative synthesis of results obtained in ACIAR Project PN 9201

Land Economics/Use,

The influence of climate change, technological progress and political change on agricultural land use: calculated scenarios for the Upper Danube catchment area

Both climate and agricultural policy changes are commonly seen as important drivers for agricultural production. In this study, scenarios of climate and political change were calculated for the Upper Danube catchment area using the regional optimization model ACRE. Two political scenarios were calculated for the year 2020. One scenario assumes the continuation of the Common Agricultural Policy reform 2003 the other assumes a strong shift away from payments of the first pillar to payments of the second pillar of the CAP. Both scenarios were combined with four different scenarios of climate change and technological progress derived from ICCP SRES assumptions and the ACCELERATES project. The results of the scenario calculations were analysed with respect to their implications for the whole catchment area as well as for selected districts. Climate change and technological progress both cause small changes in agricultural land use: fodder crop area tends to be converted to cash crop area, and intensive grasslands tend to be converted into extensive grasslands. Climate change and technological progress increase crop productivity, and consequently, total gross margin increases. The impact of climate change might get stronger toward the end of the century which is beyond the scope of the investigations presented here. The impact of climate change might thus switch from bringing net benefits in the short to medium term to bringing net losses for the area investigated in the long run.global change, regional model, climate change, agricultural policy scenarios, agricultural land use, Agricultural and Food Policy, Environmental Economics and Policy, Land Economics/Use,

Integration of all FSSIM components within SEAMLESS-IF and a stand alone Graphical User Interface for FSSIM

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,

DARCOF II. Danish research in Organic Food and Farming systems 2000-2005

The aim of this book is to present a comprehensive overview of the 41 research projects undertaken in the period 2000-2005 in the research programme DARCOF II.For each project there is a description of its background and objective in terms of which issues gave rise to the project and what the project aims to achieve. This is followed by a short description of the experiments or investigations that have been undertaken in the project. The general and applicable results derived from the project are finally described. For each project there is a reference to a project home page on www.darcof.dk. Via this page there is direct access to "Organic Eprints", which is the site containing all the project publications – both technical and scientific

Modelling the rubber tree system

An effort is made to develop a model that aims to predict the growth and production of rubber under different environmental conditions as well as different agroforestry options. The work begins with the development of the simple static model, namely Hevea Version 1.0, which acts as a precursor for development of a dynamic model. The dynamic model, which was developed using STELLA Research Software Environment and Microsoft EXCEL is then linked to the current agroforestry model WaNuLCAS (Water, Nutrients, and Light Capture in Agroforestry Systems). STELLA is the software for building system models while Microsoft EXCEL provides data analysis, list keeping, calculations as well as presentation tools. Two sub-models were added, namely a Tapping sub-model and a Tapping Panel sub-model, as a part of process to improve the efficiency of the overall model predictions. The model was run for 20 years, representing the economics life of rubber, and the outputs of the simulation were compared with observed data for validation purposes. Results from the statistical analysis showed that the model was able to simulate the girth, latex production, above-ground biomass, leaf and twigs and wood production with efficiencies (EF) of 0.83, 0.97, 0.70, -0.15 and -4.90 respectively. EF measures the accuracy of the model in performing simulation as compared to experimental data. An optimum value of EF is 1. The negative value for leaf and twigs and wood production indicated that the observed mean value is better than predicted value. An economic analysis, based on the output of the dynamic model for different rubber agroforestry system options, showed that the option of planting maize as an intercrop with rubber before tapping, followed by selling rubber wood at the end of a 20-year of rotation gave the highest Net Present Value, Internal Rate of Return, Benefit-Cost Ratio and Annual Equivalent Value compared with the option of planting rubber as monocrop

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.info:eu-repo/semantics/acceptedVersio