• Energy Research

The aim of this chapter is to assess the combined effects of 2003 CAP reform and nitrate directive on the sustainability of selected arable farming in Midi-Pyrénées. The nitrate directive is one of the earliest pieces of EU legislation aimed at controlling pollution and improving water quality across Europe. This impact analysis is performed through a bio-economic modelling framework coupling the crop model CropSyst and the farm-based model FSSIM. The 2003 CAP reform was compared first to the continuation of agenda 2000 regulations and then to a policy scenario combining the CAP reform with the application of the nitrate directive. The impact analysis shows that the potential 3% premium cut is not enough to compel farmers to adopt the nitrate directive and to substitute entirely the current activities by the alternative ones based on better N management. The farm income is marginally affected in spite of this premium cut thanks to the implementation of certain alternative activities which are more competitive. The impact on nitrate leaching is not always positive and swings between -6% to +5% depending on farm types. This implies that the partial adoption of better N management is not sufficient to ensure a reduction of leached nitrate. A sensitivity analysis shows that 17% of premium cut is required to enforce all arable farmers in the region to implement this directive. L’objectif de ce chapitre est d'évaluer les effets combinés de la réforme de la PAC de 2003 et de la directive nitrates sur la durabilité des cultures arables en Midi-Pyrénées. La réforme de la PAC de 2003 a été comparée d'abord à la poursuite de l'Agenda 2000 et ensuite à un scénario de politique combinant la réforme de la PAC de 2003 avec l'application de la directive nitrates. La modélisation bio-économique couplant le modèle de culture CropSyst et le modèle de ferme FSSIM a été mobilisée dans cette étude d’impact. Les résultats de l'analyse montrent qu’une pénalité de 3% n'est pas suffisante pour contraindre les agriculteurs à adopter la directive nitrates et pour substituer intégralement les activités courantes par des activités alternatives basées sur une meilleure gestion de l'azote. Le revenu agricole est peu affecté en dépit de cette pénalité grâce à l’adoption de certaines activités alternatives qui sont beaucoup plus rentables. L'impact sur le lessivage des nitrates n'est pas toujours positif et oscille entre -6% à 5% selon les types d'exploitation. Cela implique que l'application partielle de la directive n'est pas suffisante pour assurer une réduction des nitrates lessivés. Une analyse de sensibilité montre qu’un pourcentage de pénalité de 17% est nécessaire pour contraindre toutes les exploitations de grandes cultures de la région à adopter cette directive.

In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture.

Abstract In an uncertain socio-economic and climatic context, sustainable farming is a major challenge for farmers as well as for their agricultural advisors. It is therefore essential to develop a decision support tool (DST) that is likely to be useful to establish and evaluate new production strategies, in accordance with farm sustainability and environmental protection. This paper aims, by using the Agricultural Activity concept, to put forward and test a DST based on mathematical programming used to evaluate strategic production decisions, in conjunction with farmers and agricultural advisors. In the test case, the decisions concern the crop activities and their spatio-temporal combinations in order to reduce both the use of herbicides and the risk of weed resistance to herbicides in cereal-based production systems. Moreover, the DST considers the availability of the workforce during the crop cycle to determine the periods which are likely to require the most significant increase in comparison to the current situation. One scenario showing the current situation (Sc_baseline) and two alternative scenarios have been defined to address the weed-herbicide issue. The comparison of the scenario promoting soil tillage and the introduction of spring crops with Sc_Baseline has shown that the adoption of long-term rotations, the increase in winter crop frequency and the return to deep soil tillage have contributed to an increase in farmer income, total labor and water consumption by 7, 21 and 22% respectively. However, the intensity of pesticide use and nitrate fertilization have dropped by 15% and 17% respectively. By allowing the farmer to establish specific contracts for certain crops, the average income as well as the use of pesticide and nitrate fertilization were increased by at least 10%. This situation is the result of a simplification of rotations with a predominance of winter cereals and the elimination of deep soil tillage. The analysis of these scenarios shows that the use of the DST has made it possible not only to put forward and evaluate alternatives that result in strategic decisions but also to understand, with the concept of Agricultural Activity, the biophysical and technical processes relating to farmer decisions and their impacts at field and farm level. Understanding and sharing this functional chain at farm level is expected to strengthen the farmer-advisor relationship in order to address the complex challenges of farming system sustainability.

Scenario-based approaches in environmental and policy assessment studies are increasingly applied within integrated assessment and modelling frameworks. The SEAMLESS project develops such an integrated framework (SEAMLESS-IF) aiming to assess, ex-ante, impacts of alternative agro-environmental policies on the sustainability of agricultural systems. A particular challenge in this context is the consistent translation of a wide range of policy questions into scenarios that a modelling framework can assess. The present work defines a methodology for scenario-development in integrated policy assessment with specific emphasis on SEAMLESS-IF. After a general overview on scenario concepts for integrated policy assessment the adopted scenario concept and its development procedure is presented. They allow building integrated scenarios capturing the range of drivers of the assessed agricultural system in a consistent way across temporal and spatial scales. Then focus is on the particular procedures to translate the policy assessment questions into scenario parameters and to implement these parameters into SEAMLESS-IF. Two examples targeted at European and regional level combining integrated assessments of policy changes and technological innovations are considered to illustrate the SEAMLESS scenario concept. We conclude that the proposed methodology to translate policy assessment problems into scenarios effectively supports integrated assessment in SEAMLESS-IF or even in other modelling frameworks

Crop models and decision support system can be useful tools for researchers, teachers, scientists, extension educators, policy makers and planners to help or support the application and evaluation of sustainable and long term alternative management practices. Sunflower is an important oilseed crop that has the potential to bridge the gap between the consumption and domestic production of edible oil in developing countries such as Pakistan. Limited research has been conducted on the application of the OILCROP-SUN model for the simulation of growth and development of sunflower under different environmental conditions, especially in developing countries. Field experiments were conducted in three environments to study the impact of nitrogen (N) fertilizer on grain yield and quality. The objective of the present study was to evaluate the performance of the OILCROP-SUN model and to determine the effect of N on sunflower for three contrasting environments. The results showed that OILCROP-SUN model simulated well for growth, development and yield of sunflower hybrids in these three contrasting environments and under different management practices that included several genotypes and different nitrogen fertilizer application rates. The d values (index of agreement) for leaf area index (LAI) ranged from 88 to 97 under arid, 78 to 85 under semi- arid and 71 to 81 under sub- humid conditions. Similarly, d values for total dry matter (TDM) ranged between 90 and 94 under arid, 81 and 88 under semi-arid and 90 and 96 under sub-humid conditions, respectively while the percentage difference for grain yield ranged from 8.5 to 21% for different ecological conditions. The model also simulated LAI, TDM, grain yield and oil quality well under different environmental conditions of Pakistan and further studies should focus on application of the OILCROP-SUN model for variable agro-climatic regions where sunflower is an important crop.

The aim of this session was to present methods and tools for Integrating Assessment (IA) to predict the impact of socio-economic policies, environmental measures and technological innovations on agricultural sustainability. Most of the IA tools developed to simulate farmers’ decisions on water allocation and land-use are used for specific purposes and locations. They are often developed to handle a specific question related to environmental, socio-economic, or climate change impacts on farm sustainability. This session focused on whether it is possible to construct methods and tools to simulate, through key socio-economic and environmental indicators, the main aspects of agricultural systems from field scale to large scale by linking models and databases. Such integrated IA tools can be used to identify which agro-ecological technologies will be favored by the implementation of policies and to simulate their impacts.

Under the new oneCGIAR 2030 strategy, the CGIAR has been undertaking an integrated land-food-water systems research transformation, to deploy sustainable agricultural innovations at a faster rate for smallholder agriculture in the developing countries. France has been supporting this CGIAR reform, with a France-CGIAR action plan signed in February 2021, along three joint priority development themes: adapting and mitigating against climate change (theme coordinated by IRD), nutrition and agroecological transformation of farming and food systems in the South. Under the climate change theme, an inception 2 Degree workshop was co-organized by CGIAR and IRD in 2019. Six collaboration themes were discussed: One health; adapting to multiple stresses; climate security in the MENA region; resilient and adaptive water systems; securing Asian mega deltas; circular bioeconomy. For more, go to “BRIDGE_Origin_2019-France-CG-dialogue-integrated-climate-action”. Several France CGIAR collaborations emerged from this workshop including: A literature review « Agroecology and climate change: a case study of CCAFS research programme » published in 2020 (Andrieu N., Kebede Y., 2020). A BRIDGE concept note was submitted to the DEvelopment Smart Innovation through Research in Agriculture (DESIRA) on low-cost digital decision tools to improve water efficiency and cli-mate resilience of legume-based systems. While not successful due to tight deadline, its unique inter and transdisciplinary approach has attracted positive feedback for donors and research partners. DESIRA project “Strengthening the evidence base for a climate resilient and low-carbon small-holder agriculture through agroecology in Latin America” developed and launched in 2020, with the participation of IRD and CIRAD teams. One promising area of climate change research collaboration between France and CGIAR is the co-creation of holistic climate and water smart decision-support systems. Data revolution and more integrated model-based approaches could help water and agriculture stakeholders better evaluate the potential impact and trade-offs of various climate adaptation options to select locally adapted, robust solutions for a much needed sustainable and inclusive transformation of the agriculture and food sector. This trans- and interdisciplinary approach is called BRIDGE for co-Building Resilient climate and water smart farming systems with Interdisciplinary and Integrated models and multi-actor Decision and chanGE platforms. For more details about BRIDGE, read 1-CGIAR_BRIDGE_brief. A BRIDGE research consortium has been set up to pursue this trans and interdisciplinary research for development roadmap, composed of researchers from DIADE (Dr. Vincent Vadez, BRIDGE coordinator), UMR EspaceDev, LMI Naila, CIRAD, the International Center for Advanced Mediterranean Agronomic Studies (CIHEAM), University Mohamed VI Polytechnique (UM6P with its School of Agriculture and specialized institutes (remote sensing, water), the Institute of Environmental Geosciences (IGE) and their national research and development partners in water-scarce MENA and Sahel regions (Morocco, Tunisia, Senegal). The consortium was engaged in the CGIAR initiative on climate resilience called ClimBeR in 2021-2024, under the ClimBeR work package 3 “climate adaptation instruments / policy pathways”, led by Leeds University (PI: Dr. Andy Challinor) and ICARDA, with activities in Morocco / Tunisia and Senegal, to co-develop cross-scale water and climate resilience integrated assessment frameworks. Some teams explored multidisciplinary / integrated modelling approaches (crop, hydrological and socioeconomic in silico assessment). Others look at processes and methodologies used for the co-development of climate adaptation decision-support frameworks and tools. This dataset represents the legacy of BRIDGE work under ClimBeR, with the following information: Webinars and peer-review publications presenting the BRIDGE approach and its applications in target countries (Morocco, Tunisia, Senegal) Research and Policy recommendations from the BRIDGE collective on climate adaptation for the agriculture sector 2024-2023 activity reports and other knowledge products from the eight (8) BRIDGE teams in Morocco, Tunisia and Senegal A BRIDGE Background folder where you will find BRIDGE initial proposals per country team, background documents on the origin of BRIDGE consortium and 2022 BRIDGE knowledge products.