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A multi-model assessment of C cycling and soil C sequestration in grasslands and croplands. 6th International Symposium on Soil Organic Matter ; Harpenden (Royaume Uni)

Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner. Our qualitative evaluation suggests that as an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This will enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. This adoption requires the widespread uptake of a set of best practices as standard operating procedures., Highlights • Next generation agricultural models could benefit from recent developments in information and communications technology and data • Integrated development of modeling technologies, ICT infrastructures and user interaction is highlighted through use cases • Interoperability of data, granular models, reference data sets and requirements analysis are identified research challenges.

International audience; Previous work with crops used for livestock forage and grain production has shown that crop regrowth after grazing is influenced by grazing pressure, shoot dry matter (DM) removed and rainfall, though such findings are typically obtained using limited growing seasons. The aims of this paper were four-fold: (1) validate a wheat-grazing crop model (WHTGRAZ), (2) determine the effect of grazing regime on grain yield at the same grazing pressure. (3) determine the relationship between grazing intensity, yield and rainfall, and (4) determine how shoot DM at the start of grazing influences total DM consumption and grain yield. The ability of WHTGRAZ to predict post-grazing shoot DM accumulation was as good as or better than its ability to predict that of ungrazed crops. Aims 2-4 were investigated by simulating grazing while crops were vegetative. When the number of grazing days per hectare was predefined, yield of crops subjected to light grazing intensities for long periods were similar to yields of crops subjected to high grazing intensities for short periods. When the number of grazing days per hectare was unlimited and grazing was terminated only upon reaching a minimum shoot DM or maximum development stage, more grazing was obtained with lower grazing intensities. Irrespective of grazing intensity, crop yields were generally equal to or greater than those of ungrazed crops in years when growing season rainfall was low, but the converse occurred when growing season rainfall high. In very wet years, delaying the Start of light intensity grazing decreased total shoot DM consumption and increased yield. Alternatively, delaying the start of high intensity grazing increased shoot DM consumed and decreased yield. Two major conclusions were deducted from this study. First, grazing of rainfed wheat at relatively light intensities for long durations allows more regrowth to occur during the grazing interval. This both increases removal of shoot DM and is less likely to penalise yield. Second, the probability of grazing increasing crop productivity is greatest in years with low rainfall, regardless of grazing intensity. (C) 2012 Elsevier B.V. All rights reserved.

International audience; Existing crop-livestock models can largely be separated into two groups: those that simulate crop growth and grain yield with no grazing, and those that simulate pasture production during and after grazing but do not simulate grain yield. The purpose of this paper was to develop a crop-grazing model (hereafter, 'WHTGRAZ') capable of realistically simulating biophysical phenomena of grazed wheat crops in rain-fed environments. This derivation revealed important physiological distinctions between typical crop simulation and changes in growth that occurred as a result of grazing, such as delayed phenological development. WHTGRAZ was based on the crop model SUCROS2, with modifications. The Penman approach to evapotranspiration simulation was adapted to the FAO Penman-Monteith method. Dry matter (DM) accumulation was modified from photosynthesis to radiation-use efficiency. Specific leaf area was modelled as a function of crop ontogeny, and rules for dead leaf decomposition and trampling of shoot DM were also added. Phenological delays induced by defoliation - and consequences thereof - were identified as a key determinant of growth after grazing, and were modelled as a function of temperature and the rate of shoot DM removal. Parameters were calibrated using measurements of phenology, soil water, shoot DM, and leaf area index from field experiments using two winter wheat cultivars and four grazing treatments. Two important physiological insights were gained from the derivation. First, accurate prediction (within one SEM) of shoot DM removal during grazing is a necessary prerequisite for accurate simulation of post-grazing shoot DM accumulation. Reasonable quantification of green leaf removal and trampling by livestock is needed because green leaf area governs light interception and growth in WHTGRAZ. Second, grazing-induced delays to phenological development cause greater proportions of shoot DM to be allocated to leaves during late vegetative development, decreasing the fraction of shoot DM that would otherwise be partitioned to stems. Delayed phenology of grazed crops also causes greater proportions of kernel DM to be sourced from post-anthesis photosynthesis, as opposed to DM retranslocated from stems. (C) 2012 Elsevier B.V. All rights reserved.