Hatem Belhouchette

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.

Defining assessment projects and scenarios for policy support: Use of ontology in Integrated Assessment and Modelling

Integrated Assessment and Modelling (IAM) provides an interdisciplinary approach to support ex-ante decision-making by combining quantitative models representing different systems and scales into a framework for integrated assessment. Scenarios in IAM are developed in the interaction between scientists and stakeholders to explore possible pathways of future development. As IAM typically combines models from different disciplines, there is a clear need for a consistent definition and implementation of scenarios across models, policy problems and scales. This paper presents such a unified conceptualization for scenario and assessment projects. We demonstrate the use of common ontologies in building this unified conceptualization, e.g. a common ontology on assessment projects and scenarios. The common ontology and the process of ontology engineering are used in a case study, which refers to the development of SEAMLESS-IF, an integrated modelling framework to assess agricultural and environmental policy options as to their contribution to sustainable development. The presented common ontology on assessment projects and scenarios can be reused by IAM consortia and if required, adapted by using the process of ontology engineering as proposed in this paper.

Modelling Climate Change Impacts and Adaptation Strategies for Sunflower in Pakistan

Climate change, food security, water scarcity and environmental sustainability have all become major global challenges. As a consequence, improving resource use efficiency is an important aspect of increasing crop productivity. Crop models are increasingly being used as tools for supporting strategic and tactical decision making under varying agro-climatic and socioeconomic conditions. These tools can also support climate change assessment and the evaluation of adaptation strategies to limit the adverse impacts of climate change. In this paper, the authors report on a case study conducted to assess the potential impact of climate change on grain yield in sunflower under arid, semi-arid and subhumid conditions in the Punjab region of Pakistan. Experimental data obtained between 2008 and 2009 were used for model evaluation. The study focused on the impacts of incremental temperature change on sunflower production. The modelling suggests that grain yield could reduce by up to 15% by the 2020s with an average increase in temperature of +1°C, and by up to 25% if temperatures increased by up to 2°C for the 2050s. Adaptation strategies showed that, if the crop were sown between 14 days (for 2020) and 21 days (for 2050) earlier than the current date (last week in February), yield losses could potentially be reduced.

Evaluating integrated assessment tools for policy support

Integrated Assessment Modelling tools are complex tools requiring specific evaluation methodologies. Based on the example of the SEAMLESS-Integrated framework, we show how the conceptual, technical and system evaluation steps of the different components (procedures, quantitative models, graphic user interfaces) were performed by a multidisciplinary team. To make the not-yet-available tool real, mock-up and test cases were mobilized throughout the development process in order to integrate final end-users in the evaluation process. The main lessons from the project are that the evaluation required: (i) the use of prototypes to advance properly in the design and testing (spiral methodology); (ii) the use of case studies to stick to the end-users requirements; (iii) a proper timing of development and delivery in order to keep on schedule and leave time to the evaluation process; (iv) a multidisciplinary team of evaluators as tools are of diverse types; and (v) that it is difficult to keep independence between testers, end-users and modellers in order to guaranty transparency in the development and evaluation process.

Modelling Agri-Food Policy Impact at Farm-household Level in Developing Countries (FSSIM-Dev): Application to Sierra Leone

This report describes the generic template of a farm-household model for use in the context of developing countries in order to gain knowledge on food security and rural poverty alleviation under different economic conditions and agri-food policy options. This model, called FSSIM-Dev (Farming System Simulator for Developing Countries), is an extension of the FSSIM model developed within the SEAMLESS project. Contrary to most well-known household models which are econometric based, FSSIM-Dev is a non-linear optimization model which relies on both the general households utility framework and the farms production technical constraints, in a non-separable regime. It is referred to as a static Positive Mathematical Programming (PMP) which optimise at farm household level, with the opportunities to simulate the exchange of production factors among farm-households. FSSIM-Dev is designed to capture five key features of developing countries or/and rural areas: (i) non-separability of production and consumption decisions due to market imperfection; (ii) interaction among farm-households for market factors; (iii) heterogeneity of farm households with respect to their both consumption baskets (demand side) and resource endowments (supply side); (iv) inter-linkage between transaction costs and market participation decisions; and (v) the seasonality of farming activities and resource use. Model use is illustrated in this report with an analysis of the combined effects of rice support policy, namely fertiliser subsidy policy, and improved rice cropping managements (practices) on the livelihood of representative farm households in Sierra Leone. Results show that, first, the improvement of rice cropping managements is a key factor to boost significantly farm household income in the studied region. Second, the amount of N fertilizer required for, mainly, upland rice appears too high and costly and could not be applied by farm households without policy support (i.e. subsidies). Third, both the simulated rice policy and the improved crop managements would increase farm productivity and boost household income but they are not sufficient to fight poverty since most of the farm household types would continue to live below the extreme poverty line of 1 USD-equivalent per day.

Impact assessment of climate change on farming systems in the South Mediterranean area: a Tunisian case study

This study considers a quantitative approach for assessing the performance of Tunisian farming systems to face climate change. It is based on the resilience concept and the calculation, with a modelling chain, of three indicators: land stock, labour stock, and income flux. Two system states, “base” and “climate change”, and a time horizon of 2010–2025, are developed and compared for representative farming systems. The study shows that 55% of the farming systems were identified as being resilient to climate change. They are diversified and mostly grow cereals, vegetables, and forage crops combined with livestock, increasing their capability to mitigate climate change by reorganizing crop activities. 35% of the farms identified as being non-resilient are dominated by orchards, or cereals and orchards. They showed an important drop in farm income (−45%), mainly due to their inability to adapt their cropping systems to water stress and soil salinity. Finally, only 10% were identified as being poorly resilient farming systems. Those farms have mainly intensified cereal cropping systems based on a strategy of purchasing land to increase the surface area of profitable activities (forage and livestock). Overall, the methodology can be adapted for other dry land areas in the Mediterranean region and help experts and policy-makers to propose and test strategies for adapting to climate change.

Agricultural Activity concept for simulating strategic agricultural production decisions: Case study of weed resistance to herbicide treatments in South-West France

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.

On farm analysis of the effect of the preceding crop on N uptake and grain yield of durum wheat (Triticum durum Desf.) in Mediterranean conditions.

ABSTRACT One of the challenges of eco-efficient agriculture is the development of operational farming practices to increase the level of agricultural production, maximize the efficiency of resource use and reduce environmental impacts. Based on the efficiency frontier concept and the decomposition of resource use efficiency, we used a three-quadrant framework allowing to carry a functional analysis of the cropping system. Using a data envelope approach, we established boundary curves which represent the maximum achievable performances (yield, N uptake) when N is the only limiting factor. This framework has been first implemented and tested using published data from 112 agronomic situations of rainfed durum wheat in experimental fields in northern Syria and then further applied on a data set of 245 agronomic situations of durum wheat in farmers’ fields in two grain-producing regions of Tunisia. The results demonstrated the impact of preceding crops: durum wheat following legumes or vegetable showed a higher potential for N uptake but with only a minor effect on its conversion into grains. This positive effect of diversified rotation on potential N uptake by durum wheat is partly of-set by increased N uptake gaps in farmer’s fields indicating a higher effect of other limiting factors.

Using expert knowledge data to validate crop models on local situation data

Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfil these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20% and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green leaf area index, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.