Bergez, J.E., Colbach, N., Crespo, O., Garcia, F., Jeuffroy, M.H., Justes, E., Loyce, C., Munier-Jolain, N., and Sadok, W.
European journal of agronomy, 2010 Jan., v. 32, no. 1, p. 3-9.
farm labor, drainage, crop management, optimization, nitrogen fertilizers, cropping systems, field experimentation, simulation models, crop models, decision support systems, crops, algorithms, accuracy, and profitability
In the special issue: Cropping systems design: New methods for new challenges / edited by Jacques Wery and Johannes W.A. Langeveld. Includes references To help agricultural advisors to propose innovative crop management systems, simulation models can be a complementary tool to field experiments and prototyping. Crop management systems can be modelled either by using a vector representing dates and quantities used as input parameters in crop models or by developing specific decision models linked with biophysical models. The general design process of crop management systems by simulation follows a four-step loop (GSEC): (i) generation; (ii) simulation; (iii) evaluation; (iv) comparison and choice. The Generation step can follow different approaches: from blind generation before simulation to optimization procedures using artificial intelligence algorithms during the loop process. Simulation is mainly an engineering problem. Evaluation process means assigning a vector of indicators to the simulated crop management systems. A three-point evaluation can be carried out on the simulated crop management systems: global, agronomic and analytical. Comparison and choice of different simulated crop management systems raise the question of “monetary” versus “non-monetary” comparison and how to aggregate different quantities such as drainage, nitrogen fertilisers, labour, etc. Different examples are given to illustrate the GSEC loop on the basis of research programs conducted in France. Methodological advances and challenges are then discussed.
Tixier, P., Malezieux, E., Dorel, M., Bockstaller, C., and Girardin, P.
European journal of agronomy, 2007 Feb., v. 26, issue 2, p. 71-81.
Rpest indicator, environmental factors, environmental impact, crop management, environmental indicators, cropping systems, simulation models, crop models, Musa, bananas, risk assessment, model validation, pesticides, leaching, and water pollution
Includes references Like many intensive monocultures, some features of banana-based cropping systems may be detrimental to the environment. Pesticide use is a major cause of surface water and groundwater pollution. The risk of water pollution due to pesticides is very high in the insular areas of the French West Indies (FWI). In order to assess these risks and help design more sustainable cropping systems, we propose using an indicator to assess the risk of pesticide pollution over time (Rpest). Rpest provides dynamic assessments through a linkage with a cropping system model that simulates environmental factors and agricultural practices. An expert validation check was conducted and demonstrated that Rpest can rank cropping systems by risk as well as experts. A sensitivity analysis highlighted that the indicator can take the properties of active ingredients and pedoclimatic data into account in assessments. Rpest helps to pinpoint high pollution risk periods. It can also be used to test alternatives and compare systems. This tool can be integrated into a model-based, global, prototyping methodology used for more sustainable cropping systems.
Includes references West African cotton production has increased rapidly in recent years. Cotton is being cropped under new ecological conditions by new cotton-producing farmers, but the cropping techniques recommended by developers have essentially remained the same. Methodologies are needed to generate a broad scope of recommendations on cropping techniques to deal with the increasing diversity concerning farmers and cropping conditions. A conceptual model of a cotton field was developed that approaches a crop field as a biophysical system under the influence of a “technical system” (i.e. the combination of farmers' practices implemented in the field). The system outputs were restricted to yield and the main yield components. A theoretical model was first designed on the basis of published data and expert knowledge on cotton physiology, local soil-climate conditions and farmers' practices. It was based on five specific hypotheses on links between technical and biophysical systems. The hypotheses were tested in a local farmers' network. Thirty “cropping situations” (soil-crop-technique combinations) were selected in farmers' fields around Katogo village (Mali), a village that had been previously selected for a cotton crop management prototyping program. Homogeneous groups of situations were drawn up on the basis of the dynamics of crop aerial biomass accumulation. They were compared for their management and environment features. The initial conceptual model was then simplified, while taking the measured variability in its components and the sensitivity of the outputs to these components into account. This conceptual model is being evaluated in other villages, where we have partnerships with farmers, in order to develop a version adapted to a broad range of situations.