Cantelaube, P., & Jayet, P. (2012). Geographical downscaling of outputs provided by an economic farm model calibrated at the regional level. Land Use Policy, 29, 35–44.
Abstract: There is a strong need for accurate and spatially referenced information regarding policy making and model linkage. This need has been expressed by land users, and policy and decision makers in order to estimate both spatially and locally the impacts of European policy (like the Common Agricultural Policy) and/or global changes on farm-groups. These entities are defined according to variables such as altitude, economic size and type of farming (referring to land uses). European farm-groups are provided through the Farm Accountancy Data Network (FADN) as statistical information delivered at regional level. The aim of the study is to map locally farm-group probabilities within each region. The mapping of the farm-groups is done in two steps: (1) by mapping locally the co-variables associated to the farm-groups, i.e. altitude and land uses; (2) by using regional FADN data as a priori knowledge for transforming land uses and altitude information into farm-groups location probabilities within each region. The downscaling process focuses on the land use mapping since land use data are originally point information located every 18 km. Interpolation of land use data is done at 100 m by using co-variables like land cover, altitude, climate and soil data which are continuous layers usually provided at fine resolution. Once the farm-groups are mapped, European Policy and global changes scenarios are run through an agro-economic model for assessing environmental impacts locally.
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Özkan, Ş., & Hill, J. (2015). Implementing innovative farm management practices on dairy farms:a review of feeding systems. Turkish Journal of Veterinary and Animal Sciences, 39, 1–9.
Abstract: The Australian dairy industry relies primarily on pasture for its feed supply. However, the variability in rainfall negatively affects plant growth, leading to uncertainty in dryland feed supply, especially during periods of high milk price. New feeding (complementary) systems combining perennial ryegrass with another crop and/or pasture species may have the potential to mitigate this seasonal risk and improve productivity and profitability by providing off-season feed. To date, the majority of research studying the integration of alternative crops into pasture-based systems has focused on substitution and utilization of alternative feed sources. There has been little emphasis on the impacts of integration of forage crops into pasture-based systems. This review focuses on pasture-based feeding systems in southeastern Australia and how transitioning of systems contributes to improved productivity leading to improved profitability for dairy farmers.
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Coucheney, E., Buis, S., Launay, M., Constantin, J., Mary, B., García de Cortázar-Atauri, I., et al. (2015). Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Env. Model. Softw., 64, 177–190.
Abstract: Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved.
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Dumont, B., Leemans, V., Mansouri, M., Bodson, B., Destain, J. - P., & Destain, M. - F. (2014). Parameter identification of the STICS crop model, using an accelerated formal MCMC approach. Env. Model. Softw., 52, 121–135.
Abstract: This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. (C) 2013 Elsevier Ltd. All rights reserved.
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