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Tantari, A. (2015). The role of CAP direct payment in the support and stabilisation of farm income: empirical evidences from a constant sample of Italian farms (Vol. 5).
Abstract: The main objective of CAP Direct payments (DP) has been to increase and stabilise farm income together with a large number of additional goals (Gardner, 1991). Despite its importance, there are limited empirical evidences on farm income variability over time at farm level apart Vrolijk and Poppe (2008) and Vrolijk, de Bont, van der Veen, and Poppe (2009). The analysis focuses of the following research questions:• What is the amount of the support provided by DP? How the income of Italian farms will be affected by reductions of DP levels?• What is the extent of farm income variability over time? Is it the same in all types of farms?• Where is this variability coming from?• Do CAP direct payments reduce farm income variability? How do DP affect it? Are DP targeted to stabilise the income of those farms facing larger income variability? The analysis has been developed on the individual farms belonging to the whole Italian sample of the EU FADN farms during all years of the decade 2003-2012. The variability of farm income (FI) is assessed by calculating variance and Coefficient of Variation (CV) over the 10 year period for each farm for each relevant income component. The role of DP on income stabilisation is assessed by means of variance decomposition by income sources (Burt and Finley, 1968; El Benni and Finger, 2013; Mishra and El-Osta, 2001).The presence of DP allows for a reduction of the variability of farm income: the variability of FI when DP are not accounted for is higher than that of the whole FI. Finally, DP seem to be not targeted to those farms facing the highest level of market income variability. These results support the hypothesis that there is a potentially large room for better design the DP policy to reduce income variability. No Label
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Ahmadi, V. (2015). Impacts of Common Agricultural Policy 2015 reforms on animal health and welfare of Scottish dairy herds (Vol. 5).
Abstract: The latest Common Agricultural Policy (CAP) 2015 reforms bring a substantial change in the way farm support is paid in Scotland where previous direct CAP payments were largely based on historical entitlements. Under the new payment scheme, three rates of payment are designated based on land uses and capabilities. As a result, it is anticipated that, average large dairy farms will lose out up to 32% of their farm net margins, while small dairy farms will lose out between 7-20% of their farm net margins. Such reductions of payment support may force dairy farmers to cut costs of production on farms especially livestock variable costs including labour costs and costs of prevention, control, treatment and management of livestock diseases and welfare conditions. This will have direct and indirect consequences on health and welfare of dairy cattle. This study aims to assess the impact of new support payments under CAP 2015 reforms on financial capabilities of dairy herds in tackling three conditions namely: infertility, mastitis and lameness. A detailed inventory of 42 commercial dairy farms in Scotland that contains both physical (i.e. farm area, nutrition and labour supply, etc.) and health data collected in 2013 and was used to parameterise an optimisation model. The model is a linear programme (LP) model which optimises farm net margin under limiting farm resources. The model also consists of feed demand and supply components that are used to determine monthly feed requirements for each of the animals on a farm as well as grass yield for pasture area of the land. The model is run for both ‘healthy’ and ‘diseased’ herds under previous and future CAP support payments. Details of the model and the dataset used as well as some results will be presented at the conference. No Label
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Trnka, M., Kersebaum, K., Christian,, & Olesen, J. E. (2015). Description of the compiled experimental data available in the MACSUR CropM database (Vol. 6).
Abstract: The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) have been collected through out the project together with data for additional analysis of abiotic factors influencing yields. A list of possible dataset was collated in the first year of project however very few of the existing datasets were found usable for the crop model simulation as they fell short of the requirements defined in the part 2.3. However database has been populated as planned with the results of the ongoing MACSUR studies and will serve in the same way for the MACSUR 2 duration. No Label
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Trnka, M. (2013). Guidelines on extending on-going experiments with additional measurements to support crop modelling – Field experimental protocol (Vol. 2).
Abstract: The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) are listed. A list of possible seasonal observations/measurements that could be carried out in existing experiments to increase their potential for crop modelling studies is also provided. The general methodology suitable to be used is outlined, but in all cases the selected method depends strongly on the experimental set-up and facilities/instruments at the disposal of the experimentalists. Such methodologies needs to be documented and preferably benchmarked against standard methods. No Label
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Schils, R. (2017). Yield gaps of cereals across Europe (Vol. 10).
Abstract: The increasing global demand for food requires a sustainable intensification of crop production in low-yielding areas. Actions to improve crop production in these regions call for accurate spatially explicit identification of yield gaps, i.e. the difference between potential or water-limited yield and actual yield. The Global Yield Gap Atlas (GYGA) project proposes a consistent bottom-up approach to estimate yield gaps. For each country, a climate zonation is overlaid with a crop area map. Within climate zones with important crop areas, weather stations are selected with at least 10 years of daily data. For each of the 3 dominant soil types within a 100 km zone around the weather stations, the potential and water-limited yields are simulated with the WOFOST crop model, using location-specific knowledge on crop systems. Data from variety trials or other experiments, approaching potential or water-limited yields, are used for validation and calibration of the model. Actual yields are taken from sub-national statistics. Yields and yield gaps are scaled up to climate zones and subsequently to countries. The average national simulated wheat yields under rainfed conditions varied from around 5 to 6 t/ha/year in the Mediterranean to nearly 12 t/ha/year on the British Isles and in the Low Countries. The average actual wheat yield varied from around 2 to 3 t/ha/year in the Mediterranean and some countries in East Europe to nearly 9 t/ha/year on the British Isles and in the Low Countries. The average relative yield gaps varied from around 10% to 30% in many countries in Northwest Europe to around 50% to 70% in some countries in the Mediterranean and East Europe. The paper will elaborate on results per climate zone and soil type, and will also include barley and maize. Furthermore we will relate yield gaps to nitrogen use.
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