Assessing the risk of farmland abandonment in the EU

An expert panel of European scientists in fields related to land abandonment (bio-physical / land suitability, farm structure, farm economics, land market, regional development, socio and economic factors in rural areas) were tasked to identify main drivers of farmland abandonment in Europe. Two sets of criteria for assessing the risk have been suggested: Low farm stability and viability was estimated through drivers on ‘low farm income’ (D2), ‘lack of investments on the farm’ (D3), ‘farm-holder’s age’ (D4), ‘farm manager qualifications’ (D5), ‘low farm size’ (D8), ‘commitments taken by farmers in specific management scheme’ (D9). Negative regional context was estimated through indicators on ‘weak land market’ (D1), ‘low population density and remoteness’ from market opportunities and services (D7). Each of these drivers was calculated individually; an assessment was done to provide relevance and robustness of results, corresponding maps were produced. The results suggested a first group of powerful drivers (policy relevance, analytical soundness, data availability and robustness) composed of: ‘weak land market’ (D1), ‘low farm income’ (D2), ‘low density population and remoteness’ (D7). The second group of drivers with ‘lack of investments on the farm’ (D3) and ‘farm-holder’s age’ (D4) were policy relevant but reliability was lower when using European datasets. The third group of drivers (‘farm manager qualifications’ (D5), ‘low farm size’ (D8), and ‘commitments taken by farmers in specific management scheme’ (D9)) showed some deficiencies in analytical soundness and/or data reliability. They were not further used in the analysis. In order to produce a risk indicator of ‘farmland abandonment’, composite indices were developed based on Principal Component Analysis carried-out on the normalised values of the individual drivers. The normalisation procedure was performed at two different levels: (a) EU27 level as an attempt to elaborate a risk index covering EU27 in an homogeneous manner; and (b) MS level. For the composite indices, further analysis was done at NUTS2 level to relate those flagged with higher risk to the holding’s farm-types. It results that extensive and traditional farming systems with high proportions of permanent crops or permanent grasslands are the most frequent farm-types found in NUTS2 at risk.JRC.H.4-Monitoring Agricultural Resource

Soil fertility comparison among organic and conventional managed citrus orchards in Sicily.

In recents years, organic farming is expanding in Southern Italy and in the Mediterranean area, due to interest of consumers and EU agricultural policies. Evaluation of organic farming system introduction on soil quality and fertility status should be taken into account in order to define medium-long term environmental and agricultural strategies, on both locol and national scale. Soil physical, chemical and biological parameters represent effective tools to evaluate soil quality and changes in soil fertility status, as a consequence of different agronomic management (i.e. organic vs coventional). The aim of this work was to compare soil fertility of conventional and organic managed citrus Orchards, using specific soil system descriptors. The research was carried out in a Mediterranean environment (Sicil Region, Southern Italy), on Navelina and Tarocco orchards. Soil characteristics were analysed in 54 farms under both organic and conventional management. Farms were selected to obtain similar pairs (27) in the same environmental conditions. Moreover, orchards pairs were homogeneous for age, cultivar and rootsock to reduce effects not linked to soil management. For each soil, total organic carbon, total nitrogen, mineral NO3-N and NH4-N were determined. In addition, in order to evaluate biological fertility of the considered soils, carbon mineralisation and nitrogen mineralisation in anaerobic conditions were studied. Soils' humic fraction was at least characterised qualitatively by isoelectric focusing technique,to obtain information on soil organic matter stability. Potenzially mineralisable carbon and cumulative mineralised carbon determined on 21 days experimental trials differed significantly in organic and conventional soils. Since this significance was detected also for cumulative mineralised carbon after 7 days, probably C-mineralisation represents the more reliable and prompter indicator to discriminate soil biological fertility with respect to the other tested ones. Moreover, it should be remarked that some parameters (as total nitrogen content, mineralised carbon after 1 days and the more humified organic matter fraction), even if not yet significantly different, revealed a strong tendency to increase in organic managed soils, attesting that the organic citrus orchards can be considered systems able to conserve energy and store nutrients more than the conventional ones

Final technical report: Certification of low carbon farming practices

In 2010, the European Parliament asked the European Commission to carry out a pilot project on the “certification of low-carbon farming practices in the European Union” to promote reductions of GHG emissions from farming. The overall aim of the project was to assess how efforts of European farmers to produce agricultural products with carbon-neutral or low-carbon-footprint farming practices might be incorporated into policy approaches (possibly via certification), so as to promote the reduction of GHG emissions from agriculture. The project included: i) a review of existing farm-level lifecycle-based climate-related certification and labelling schemes, ii) the development and testing of a user friendly open-source carbon calculator suitable for assessing the lifecycle GHG emissions from different types of farming systems across the whole EU, and iii) the design/assessment of policy options for promoting low-carbon farming practices.JRC.H.4-Monitoring Agricultural Resource

Machine learning for regional crop yield forecasting in Europe

Crop yield forecasting at national level relies on predictors aggregated from smaller spatial units to larger ones according to harvested crop areas. Such crop areas come from land cover maps or reported statistics, both of which can have errors and uncertainties. Sub-national or regional crop yield forecasting minimizes the propagation of these errors to some extent. In addition, regional forecasts provide added value and insights to stakeholders on regional differences within a country, which would otherwise compensate each other at national level. We propose a crop yield forecasting approach for multiple spatial levels based on regional crop yield forecasts from machine learning. Machine learning, with its data-driven approach, can leverage larger data sizes and capture nonlinear relationships between predictors and yield at regional level. We designed a generic machine learning workflow to demonstrate the benefits of regional crop yield forecasting in Europe. To evaluate the quality and usefulness of regional forecasts, we predicted crop yields for 35 case studies, including nine countries that are major producers of six crops (soft wheat, spring barley, sunflower, grain maize, sugar beets and potatoes). Machine learning models at regional level had lower normalized root mean squared errors (NRMSE) and uncertainty than a linear trend model, with Wilcoxon p-values of 3e-7 and 2e-7 for 60 days before harvest and end of season respectively. Similarly, regional machine learning forecasts aggregated to national level had lower NRMSEs than forecasts from an operational system in 18 out of 35 cases 60 days before harvest, with a Wilcoxon p-value of 0.95 indicating similar performance. Our models have room for improvement, especially during extreme years. Nevertheless, regional crop yield forecasts from machine learning and aggregated national forecasts provide a consistent forecasting method across spatial levels and insights from regional differences to support important policy decisions

MARS Bulletin Vol 17 No 1

The annexed document is the template for the bulletin that will be issued on the 10th March. This bulletin covers meteorological analysis and crop yield forecasts for the period 21 November 2008 - 28 February 2009 (since the day after the last covered period, to the last day of the decade before)JRC.G.3-Monitoring agricultural resource

Crop monitoring in Europe - MARS Bulletin Vol. 23 No 10 (2015) - Difficult start for winter crops in Eastern and Northern Europe

Yield forecasts for summer crops at EU-28 level remain low and are comparable to last month’s forecast. September was warmer than usual in northern, eastern and south-eastern Europe and colder than usual in western Europe. October has generally been colder than usual so far, especially during the second dekad, when negative minimum temperatures occurred in many areas of central and eastern Europe. Wetterthan- usual conditions were recorded in south-eastern Europe, south-eastern France, central and southern Italy, northern Germany and several parts of northern Europe. Large areas in south-eastern Europe faced a period of abundant rains slowing down the harvesting activities of maize and sunflower and hampering the sowing of winter crops. Dry conditions have persisted in Poland, Lithuania, western Ukraine and southern Russia. In these regions, the winter crops sown in September germinated under unfavourable conditions which further worsened due to the low temperatures that occurred in October. The sowing of winter cereals has progressed without major problems in the EU’s largest producing states, France, Germany and the UK.JRC.H.4-Monitoring Agricultural Resource

MARS Bulletin Vol. 23 No 7 (2015) - Grain maize outlook worsened due to heat waves and drought

Crop monitoring in EuropeJRC.H.4-Monitoring Agricultural Resource

ISO -LWS two-colour diagram of young stellar objects

We present a [60-100] versus [100-170]μm two-colour diagram for a sample of 61 young stellar objects (YSOs) observed with the Long Wavelength Spectrometer (LWS) on-board the Infrared Space Observatory (ISO). The sample consists of 17 Class 0 sources, 15 Class I, nine Bright Class I (Lbol>104Lsolar) and 20 Class II (14 Herbig Ae/Be stars and six T Tauri stars). We find that each class occupies a well-defined region in our diagram with colour temperatures increasing from Class 0 to Class II. Therefore the [60-100] versus [100-170] two-colour diagram is a powerful and simple tool to derive from future (e.g. with the Herschel Space Observatory) photometric surveys the evolutionary status of YSOs. The advantage over other tools already developed is that photometry at other wavelengths is not required: three flux measurements are enough to derive the evolutionary status of a source. As an example we use the colours of the YSO IRAS 18148-0440 to classify it as Class I. The main limitation of this work is the low spatial resolution of the LWS which, for some objects, causes a high uncertainty in the measured fluxes due to background emission or to source confusion inside the LWS beam

Strong H_2O and high-J CO emission towards the Class 0 protostar L1448-mm

The spectrum of the Class 0 source L1448-mm has been measured over the wavelength range extending from 6 to 190 mu m with the Long Wavelength Spectrometer (LWS) and the Short Wavelength Spectrometer (SWS) on the Infrared Space Observatory (ISO). The far infrared spectrum is dominated by strong emission from gaseous H_2O and from CO transitions with rotational quantum numbers J >= 14; in addition, the H_2 pure rotational lines S(3), S(4) and S(5), the OH fundamental line at 119 mu m, as well as emission from [O I] 63 mu m and [C Ii] 158 mu m are also observed. The strong CO and water emission can be consistently explained as originating in a warm gas component at T ~ 700-1400 K and n_H_2 ~ (3-50) 10(4 ) cm(-3) , which fills about 0.2-2% of the ~ 75\arcsec LWS field of view (corresponding, assuming a single emitting region, to a physical size of about (3-12)\arcsec or (0.5-2) 10(-2) pc at d = 300 pc). We derive an H_2O/CO abundance ratio ~ 5, which, assuming a standard CO/H_2 abundance of 10(-4) , corresponds to H_2O/H_2 ~ 5 10(-4) . This value implies that water is enhanced by about a factor ~ 10(3) with respect to its expected abundance in the ambient gas. This is consistent with models of warm shocked regions which predict that most of the free atomic oxygen will be rapidly converted into water once the temperature of the post-shocked gas exceeds ~ 300 K. The relatively high density and compact size inferred for this emission may suggest an origin in the shocked region along the molecular jet traced by SiO and EHV CO millimeter line emission. Further support is given by the fact that the observed enhancement in H_2O can be explained by shock conditions similar to those expected to produce the abundant SiO observed in the region. L1448-mm shows the largest water abundance so far observed by ISO amongst young sources displaying outflow activity; we argue that the occurrence of multiple shocks over a relatively short interval of time, like that evidenced in the surroundings of L1448-mm, could have contributed to enrich the molecular jet with a high H_2O column density. Based on observations with ISO, an ESA project with instruments funded by ESA Member States (especially the PI countries: France, Germany, the Netherlands and the United Kingdom) with the participation of ISAS and NAS

JRC MARS Bulletin: Crop monitoring in Europe: February 2018

Winter conditions so far present no threat to winter cropsJRC.D.5-Food Securit