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Grain legumes in cropping systems result in agronomic and environmental benefits. Nevertheless, their areas in Europe have strongly decreased over the past decades. Our aim was to design locally adapted innovative cropping systems including grain legumes for three European local pedoclimatic contexts, to assess their sustainability, and to discuss their feasibility with stakeholders. The methodology included an initial diagnosis of the most frequent cropping systems and local improvement targets in each local context (e.g. improve legume profitability, limit diseases of legumes, reduce intensive use of chemical inputs in cropping systems), the design of innovative legume-based cropping systems during a common workshop, focusing on three aims ((i) decrease pesticide use, (ii) reduce mineral N fertiliser dependency, and (iii) increase yield stability of grain legume crops and other crops of the crop sequence), and their multicriteria sustainability assessment. Stakeholders meetings were organised in each local context to discuss the feasibility of implementing the innovative cropping systems in farmers' fields (technical implementation of cropping systems and possibility of development of legume sectors). Four to five cropping systems were designed in each local context, with crop sequences longer than references. They included at least two grain legumes (pea, faba bean, chickpea, lentil or lupine), as sole crops or intercropped with cereals. Overall sustainability was similar or improved in 71% of the legume-based cropping systems compared with their corresponding references. Among the designed cropping systems, stakeholders identified feasible ones considering both technical issues and development of legume sectors. The results indicate that reintegrating more grain legumes in the three European local contexts tested will contribute to more sustainable farming systems. [ABSTRACT FROM AUTHOR]

Pesticides pose serious threats to both human health and the environment. In Europe, farmers are encouraged to reduce their use, and in France a recent environmental policy fixed a target of halving the pesticide use by 2018. Organic and integrated cropping systems have been proposed as possible solutions for reducing pesticide use, but the effect of reducing pesticide use on crop yield remains unclear. Here we use a set of cropping system experiments to quantify the yield losses resulting from a reduction of pesticide use for winter wheat in France. Our estimated yield losses resulting from a 50% reduction in pesticide use ranged from 5 to 13% of the yield obtained with the current pesticide use. At the scale of the whole country, these losses would decrease the French wheat production by about 2 to 3 millions of tons, which represent about 15% of the French wheat export. [ABSTRACT FROM AUTHOR]

In a context of economic and environmental concerns in agriculture, legumes appear to be suitable alternative crops to diversify current cropping systems and reduce their dependence on synthetic nitrogen (N) fertiliser and protein from imported soya bean. However, legume-based cropping systems may increase N losses through nitrate leaching if the N available from legumes does not coincide with subsequent crop requirements. To help agricultural advisers manage N in these systems, we adapted the decision-support system Syst'N®, designed to assess N losses in cropping systems, to simulate three annual and one perennial legume crops: pea, faba bean, soya bean and lucerne. To this end, we adapted and simplified existing submodels of legume functioning to include them in Syst'N, to keep the latter simple. We adapted the submodels "BNF" (i.e. biological N fixation) from the STICS model and "dormancy" from the CropSyst model. We also added the ability to enter the flowering date to improve predictions (improvement in N fixation's rRMSE from 57% to 41% and EF from 0.57 to 0.77). The equations and associated parameter set developed for the four legume crops yielded satisfying predictions of crop biomass (rMBE = 9%, EF = 0.82, rRMSE = 39%) and N content (rMBE = 5%, EF = 0.76, rRMSE = 37%). These performances support the philosophy of Syst'N® that requires minimising the number of additional parameters for users when representing new crops or processes. • The decision-support system Syst'N® was adapted to simulate diverse legume crops. • Submodels of legume functioning were adapted and simplified to keep Syst'N® simple. • The ability to enter the flowering date to improve predictions was added. • Predictions of soil water and N content were less accurate. [ABSTRACT FROM AUTHOR]

• We developed a Decision-support systems (DSS) for integrated weed management systems. • Farmers and crop advisors took part via online surveys, group meetings and workshops. • The DSS goal, application field and structure was defined with future users. • They proposed two DSS, based on meta-decision rules or detailed farming practices. • Biophysical knowledges were fed to the DSS from an existing mechanistic model. Farmers and farm advisors need a decision support system (DSS) to develop multiperformant weed management strategies adapted to economic, social and environmental stakes and farmers' constraints. We worked with future users, farmers, and crop advisors to define the uses and type of DSS they needed, via an online survey, group meetings and workshops. The feedback from future users helped to define the structure of the DSS with two complementary DSS needed: (1) a synthetic tool working with meta-decision rules to help with a complete overhaul of a cropping system, and (2) a precise and detailed tool for fine tuning cropping systems. Here, we present how we interacted with future users to transform an existing research model into a DSS by (1) defining its goal, application field and structure, (2) entering into the DSS knowledge on biophysical processes comprised in the mechanistic weed dynamics model FlorSys. We selected a more appropriate vocabulary for describing agricultural practices and formats with future users. Based on their feedback, a large range of weed impact indicators was included in the DSS so that farmers can choose the most pertinent for their objectives. Based on workshops with farmers, a decision tree format with numerical values of weed impact indicators was chosen to demonstrate the impacts of multiple cultural practices combinations. The DSS also includes an online calculator predicting weed (dis)services from meta-decision rules which was tested by crop advisors. Responses of the DSS were sometimes not expected by users, but were still considered interesting highlighting the need of agronomical support while using the tool. [ABSTRACT FROM AUTHOR]