Anne Gobin

Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these.

Adverse weather conditions greatly reduce crop yields, leading to economic losses and lower food availability. The characterization of adverse weather and the quantification of their potential impact on arable farming is necessary to advise farmers on feasible and effective adaptation strategies and to support decision making in the agriculture sector. This research aims to analyze the impact of adverse weather on the yield of winter wheat, grain maize and late potato using a yield gap approach. A time-series analysis was performed to identify the relationship between (agro-)meteorological indicators and crop yields and yield gaps in Flanders (northern Belgium) based on 10 years of field trial and weather data. Indicators were calculated for different crop growth stages and multiple soils. Indicators related to high temperature, water deficit and water excess were analyzed, as the occurrence frequency and intensity of these weather events will most likely increase by 2030–2050. The ...

Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak p...

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here we use joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical input across the country, Sentinel-1 12-day backscatter composites were created after incidence angle normalization, and Sentinel-2 NDVI images were smoothed to yield dekadal cloud-free composites. An optimized random forest classifier predicted the 8 crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increas...

A one-dimensional simulation model that simulates daily mean soil temperature on a daily time-step basis, named AGRISOTES (AGRIcultural SOil TEmperature Simulation), is described. It considers ground coverage by biomass or a snow layer and accounts for the freeze/thaw effect of soil water. The model is designed for use on agricultural land with limited (and mostly easily available) input data, for estimating soil temperature spatial patterns, for single sites (as a stand-alone version), or in context with agrometeorological and agronomic models. The calibration and validation of the model are carried out on measured soil temperatures in experimental fields and other measurement sites with various climates, agricultural land uses and soil conditions in Europe. The model validation shows good results, but they are determined strongly by the quality and representativeness of the measured or estimated input parameters to which the model is most sensitive, particularly soil cover dynamic...

SummaryWater is a key resource for human activities and a critical trigger for the welfare of the whole society. The agricultural sector makes up the main share in global freshwater consumption and is therefore responsible for a large part of the water scarcity in many drought prone regions. As an indicator that relates human consumption to global water resources, the “Water Footprint” (WF) concept can be used, where in case of crop production the total consumed water of crop fields for the crop growing seasons is related to the harvested dry matter crop yield (such as grains). In our study, we simulated the green and primary blue WF of selected main crops for Austrian conditions. Different irrigation scheduling scenarios, demonstrated for a main agricultural production area and various crops in Austria with significant irrigation acreage, were studied. The impact of climate and soil conditions on the green crop WFs of reference crops over the whole territory of Austria were simulat...

Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, trends in landscape attractiveness may be linked to certain policies, or the absence of policies over time. A spatial and temporal evaluation of landscape attractiveness is thus desirable for landscape policy development. In this paper, landscape attractiveness was spatially evaluated for Flanders (Belgium) using landscape indicators derived from geospatial data as a case study. Large local differences in landscape quality in (i) rural versus urban areas and (ii) between the seven agricultural regions in Flanders were found. This observed spatial variability in landscape attractiveness demonstrated that a localized approach, considering the geophysical ch...

Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regressio...

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural manag...

We present the development of a thermal IR camera for a nanosat In Orbit Demonstration (IOD) mission. The primary objective of the TIR remote sensing mission is Land Surface Temperature (LST) and derived evapotranspiration (ET). On the longer term we aim at building up a constellation of satellites allowing a fast revisit time in the order of one to a couple of days.

SummaryWater is a key resource for human activities and a critical trigger for the welfare of the whole society. The agricultural sector makes up the main share in global freshwater consumption and is therefore responsible for a large part of the water scarcity in many drought prone regions. As an indicator that relates human consumption to global water resources, the “Water Footprint” (WF) concept can be used, where in case of crop production the total consumed water of crop fields for the crop growing seasons is related to the harvested dry matter crop yield (such as grains). In our study, we simulated the green and primary blue WF of selected main crops for Austrian conditions. Different irrigation scheduling scenarios, demonstrated for a main agricultural production area and various crops in Austria with significant irrigation acreage, were studied. The impact of climate and soil conditions on the green crop WFs of reference crops over the whole territory of Austria were simulat...

Modelling biomass production and the environmental impact of short rotation coppice (SRC) plantations is necessary for planning their deployment, as they are becoming increasingly important for global energy production. This paper describes the modification of the widely used land surface model ORCHIDEE for stand scale simulations of SRC plantations. <br><br> The model uses weather data, soil texture and species-specific parameters to predict the aboveground (harvestable) biomass production, as well as carbon and energy fluxes of an SRC plantation. Modifications to the model were made to the management, growth, and allocation modules of ORCHIDEE. <br><br> The modifications presented in this paper were evaluated using data from two poplar based SRC sites. The simulations show that the model performs very well to predict aboveground (harvestable) biomass production (within measured ranges), ecosystem photosynthesis (<i>R</i><sup>2</sup> ...

European agri-environment programmes are based on the common principle that farmers deliver environmental services for which society pays. Due to the voluntary nature of agri-environment measures (AEM), the issue of farmers motives or reasons for participation has been an important topic of investigation in past years. The present paper examines farmers rationale for participation in AEM against the backdrop of continued debate over whether to develop relatively simple measures that can be readily applied by many farmers or give greater priority to measures that are more targeted - i.e. to the specific management requirement of particular habitats or species - but are often more complex. The paper draws on empirical material from a case study in the Dyle valley, Belgium, including in-depth interviews, expert consultations and a mail survey. It was sought not only to identify and quantify the importance of separate reasons for participation, but also to reveal how these reasons and other elements of relevance were logically interrelated in the explanation that farmers themselves give for their participation. As a result, six modes or styles of participation were identified: opportunistic, calculative, compensatory, optimising, catalysing and engaged. The analyses suggest that there were notable differences in that both separate reasons for and modes of participation do vary with the complexity of the measures requirements. Overall, the study demonstrates that participation in AEM is not simply a matter of weighing the money against the effort for adoption. Whereas money is an important driver for participation (in particular, for those adopting complex AEM) it plays widely differing roles depending on the level of farmers reasoning (farm enterprise, single practice or landscape feature) and the importance they give to other considerations (environmental effect, production potential of land, goodness of fit, etc.). Practical implications are drawn for both policy makers and programme managers who develop and make available tailor-made support.

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ABSTRACT Devastating weather-related events recorded in recent years have captured the interest of the general public in Belgium. Extreme weather events such as droughts, heat stress, rain storms and floods are projected to increase both in frequency and magnitude with climate change. Since more than half of the Belgian territory is managed by the agricultural sector, extreme events have significant impacts on agro-ecosystem services and pose severe limitations to sustainable agricultural land management. The perspective of rising risk-exposure is exacerbated further by more limits to aid received for agricultural damage (amendments to EC Regulation 1857/2006) and an overall reduction of direct income support to farmers. Current knowledge gaps related to the occurrence of extreme events and the response of agro-ecosystems need to be addressed in conjunction with their vulnerability, resilience and adaptive possibilities. A chain of risks approach starts with assessing the likely frequency and magnitude of extreme meteorological events by means of probability density functions. Impacts are subsequently based on physically based models that provide information on the state of the damage at any given time and assist in understanding the links between different factors causing damage and in determining bio-physical vulnerability. The output of regional bio-physical models is compared with remote sensing based algorithms applied on SPOT-VGT temporal data. Crop damage and risk indicators are derived from remote sensing, meteorological records, crop modelling and agricultural statistics and compared to damage statistics obtained from the government-based agricultural disaster funds. Damages due to adverse meteorological events are strongly dependent on crop type, crop stage and soil type. Spatio-temporal indicators of drought during the growing season and waterlogging at harvest showed the highest agreement with damage, followed by hail and frost. In general potatoes, flax and rape seed are the most vulnerable crops, followed by cereals and sugar beets.

Soil erosion is a natural process, occurring over geological time, and most concerns about erosion are related to accelerated erosion, where the natural rate has been significantly increased by human activity. Soil erosion poses severe limitations to sustainable agricultural land use, as it reduces on-farm soil productivity and causes the accumulation of sediments and agro-chemicals in waterways. In Europe, soil erosion is caused mainly by water and, to a lesser extent, by wind. Rill and inter-rill erosion affects the largest area, but evidence of ...

ABSTRACT Agricultural land use and crop rotations can greatly affect the amount of carbon sequestered in the soil. We developed a framework for modelling the impacts of crop rotations on soil carbon sequestration at the field scale with test case Flanders. A crop rotation geo-database was constructed covering 10 years of crop rotation in Flanders using the IACS parcel registration (Integrated Administration and Control System) to elicit the most common crop rotation on major soil types in Flanders. In order to simulate the impact of crop cover on carbon sequestration, the Roth-C model was adapted to Flanders' environment and coupled to common crop rotations extracted from the IACS geodatabases and statistical databases on crop yield. Crop allometric models were used to calculate crop residues from common crops in Flanders and subsequently derive stable organic matter fluxes to the soil (REGSOM). The REGSOM model was coupled to Roth-C model was run for 30 years and for all combinations of seven main arable crops, two common catch crops and two common dosages of organic manure. The common crops are winter wheat, winter barley, sugar beet, potato, grain maize, silage maize and winter rapeseed; the catch crops are yellow mustard and Italian ryegrass; the manure dosages are 35 ton/ha cattle slurry and 22 ton/ha pig slurry. Four common soils were simulated: sand, loam, sandy loam and clay. In total more than 2.4 million simulations were made with monthly output of carbon content for 30 years. Results demonstrate that crop cover dynamics influence carbon sequestration for a very large percentage. For the same rotations carbon sequestration is highest on clay soils and lowest on sandy soils. Crop residues of grain maize and winter wheat followed by catch crops contribute largely to the total carbon sequestered. This implies that agricultural policies that impact on agricultural land management influence soil carbon sequestration for a large percentage. The framework is therefore suited for further scenario analysis and impact assessment in order to support agri-environmental policy decisions.