Anne Merot

SPFC: a tool to improve water management and hay production in the Crau region

This article deals with the development and application of SPFC, a model used to improve water and grassland production (HC) in this region of France. This model is composed of two sub-models: an irrigation model and a crop model. As the fields are border-irrigated, these two sub-models are coupled. The crop model simulates dry matter (DM), leaf area index (LAI) and soil water reserve (SWR) variations. LAI and SWR are both used for border model updating: SWR for the deficit of saturation required by the infiltration equation and LAI for the roughness coefficient n. After calibration and validation, SPFC is then used to identify realistic management strategies for the irrigation and production system at the plot level. By scheduling irrigation when SWR is 50% depleted, would result in a low Dry Matter DM production loss (around 10%), reduced labour (eight irrigation events instead of 11) and in significant water saving compared with farmers’ practices, on the basis of an average climatic scenario. Furthermore, this improvement of irrigation efficiency is not incompatible with groundwater recharge used for the potable water supply of the region.

IRRIGATE: A dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management

Water management practices in southern France (the Crau plain) need to be modified in order to ensure greater water use efficiency and less environmental damage while maintaining hay production levels. Farmers, water managers and policy makers have expressed the need for new methods to deal with these issues. We developed the biodecisional model IRRIGATE to test new irrigation schedules, new designs for water channels or fields and new distribution planning for a given water resource. IRRIGATE simulates the operation of a hay cropping system irrigated by flood irrigation and includes three main features: (i) border irrigation with various durations of irrigation events and various spatial orders of water distribution, (ii) species-rich grasslands highly sensitive to water deficit, (iii) interactions between irrigation and mowing. It is based on existing knowledge, adapted models and new modules based on experiments and survey data. It includes a rule-based model on the farm scale, simulating dynamically both irrigation and mowing management, and two biophysical models. The two biophysical models are a dynamic crop model on the field scale simulating plant and soil behaviour in relation to water supply, and a flood irrigation model on the border scale simulating an irrigation event according to plant and hydraulic parameters. Model outputs allow environmental (water supply, drainage), social (labour) and agronomic (yields, water productivity and irrigation efficiency) analyses of the performance of the cropping system. IRRIGATE was developed using firstly a conceptual framework describing the system modelled as three sub-systems (biophysical, technical, and decision) interacting within the farm. Then a component-based spatially explicit modelling based on the identification of the interactions between modules, the identification of temporal and spatial scales of modules and the re-use of previous models was used to develop the numerical model. An example of the use of the biodecisional model is presented showing the effects on a real farm of a severe water shortage in 2006.

Converting to organic viticulture increases cropping system structure and management complexity

Organic viticulture is an effective cultivation method that can reduce the environmental impacts of grape growing while maintaining profitability. For some vineyards, simple adjustments can suffice to make the conversion to organic farming; however, for most, major changes in system structure and management must be implemented. Here, we showed for the first time that converting to organic viticulture impacts vineyard complexity. We used six complexity indicators to assess modifications to cropping system structure and management: number of fields, number of difficult-to-manage fields, vineyard area, number of field interventions, number of technical management sequences, and number of management indicators. These six indicators were assessed through interviews carried out with winegrowers from 16 vineyards between 2008 and 2012. Changes in vineyard performances during conversion were also measured. We demonstrate that conversion to organic viticulture increased the complexity of vineyard structure and management for the 16 vineyards surveyed. While this increase allowed agronomic performances in all vineyards to be maintained, it also came with an increase in labor requirements (of up to 56%) compared to conventional agriculture. We conclude that the six indicators are appropriate for assessing changes in vineyard complexity and could be extended to all agricultural systems to better anticipate the implications of organic farming conversion for a farm’s biophysical, technical, and decisional subsystems.