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

Multi-functional land use is not self-evident for European farmers: a critical review.

Soils perform more functions than primary productivity. Examples of these functions are the recycling of nutrients, the regulation and purification of water, the regulation of the climate, and supporting biodiversity. These abilities are generally referred to as the soil quality. Soil management that favors primary productivity may have positive and negative impacts on the other functions, and vice versa, depending on soil and climatic conditions. All these functions are under pressure, particularly in intensive agriculture. In the absence of mandatory regulations, most European farmers give limited attention to other functions than primary productivity in spite of recommendations by scientists, society and policy makers to acknowledge the ecosystem services provided by soils. The present paper analyses the underlying causes of this limited attention for the multi-functionality of soils by farmers. It is concluded that their focus on primary productivity may stem from (1) insufficient visible proof for soil degradation and benefits of preventive measures over curative measures, (2) limited awareness or conviction of long-term synergies, (3) insufficient remuneration of ecosystem services by society or compensation of yield penalties in favor of these services, (4) lacking trustworthy knowledge about and support for multi-functional soil management, and (5) absence of incentives and regulations on soil management and their enforcement. All these shortcomings need to be addressed by advisors, scientists, and policy makers, whilst acknowledging the need for underpinning and differentiation of incentives and regulations.</p

Simulation of potential yields of new rice varieties in the Senegal River Valley

Irrigated rice in the Sahel has a high yield potential, due to favorable climatic conditions. Simulation models are excellent tools to predict the potential yield of rice varieties under known climatic conditions. This study aimed to (1) evaluate new rice genotypes for the Sahel, and (2) calibrate simulation models to predict potential yield of irrigated rice in the Sahel. Two new inbred lines (ITA344 and IR32307) and one O. sativa × O. glaberrima line (WAS 161-B-9-2) were tested against IR64, an international check, and Sahel 108, locally the most popular rice cultivar. Field experiments were executed at two sites along the Senegal river, Ndiaye and Fanaye, differing in temperature regime and soil type. All cultivars were sown and transplanted at two sowing dates in February and March 2006. Observed grain yields varied from 7 to 10 t ha-1 and from 6 to 12 t ha-1 at Ndiaye and Fanaye, respectively. The number of days until maturity ranged from 119 to 158, depending on cultivar, sowing date and site. Experimental data of one sowing date was used to calibrate both the DSSAT and ORYZA2000 models. According to ORYZA2000, the same cultivars needed 400°Cd more in Fanaye than in Ndiaye to complete their cycle. ORYZA2000 simulated phenology well, but yield was underestimated. After calibrating DSSAT, different sets of genetic coefficients gave similar results. Genetic coefficients that reflected the observed phenology well resulted in lower than observed yields. Crop growth simulation is a powerful tool to predict yields, but local calibration at the same sowing date is needed to obtain useful result