Farm-level adaptation to climate change: The case of the Loam region in Belgium

Few studies have addressed the topic of farmers' adaptation to climate change from a multidisciplinary perspective, because of the difficulty in assessing their impacts. In view of the growing concern in the agricultural sector on this issue, we analyzed farm-level adaptation through arable land-use changes in the specific case of the Loam region in Belgium. With this aim, we used an agro-economic model which considered 20-year series of current and projected simulated yields with and without considering additional farming practices to reduce crop stress, such as irrigation and soil and water conservation techniques. Agronomic results show that climate change will negatively affect summer crop yields, particularly sugar beet and potatoes. However, we also show that adaptation to climate change through land-use changes can compensate for crop yield losses and lead to utility gains. These are obtained by reducing the share of land allocated to summer crops and barley and by increasing the surface allocated to less vulnerable crops such as winter wheat. Finally, irrigation practices would not be justified in the Loam region under climate change, since their use would incur important financial costs for farmers

Agriculture in a changing climate : Preface

Agriculture is an economic sector that is particularly sensitive to weather. Recent meteorological events have reduced crop production in different parts of the world. Weather extremes resulting from climate change are projected to increase, making crop production more vulnerable and ultimately threatening food security. A continuing effort to develop scientific knowledge in climate and agriculture will help to manage risks and opportunities in these fields. This special issue of Climate Research represents a collection of studies that contribute to the general understanding of weather-related risks and climate impacts on agricultural system

A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time

Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal information to assist such yield forecasts. This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series (5daily at 100 m spatial resolution) or spatially coarser but denser (300 m and 1 km at daily frequency) time series. Furthermore, we hypothesised that better yield estimations could be made using thermal time, which is closer to the crop physiological development. Time series of NDVI from the PROBA-V instrument, which has delivered images at a spatial resolution of 100 m, 300 m and 1 km since 2013, were extracted for 39fields for field and 56fields for regional level analysis across Northern France during the growing season 2014-2015. An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field. The fitted model was subsequently integrated either over thermal time or over calendar time, using different baseline NDVI thresholds to mark the start and end of the cropping season. These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level. The dependency of this relationship on the spatial pixel purity was analysed for the 100 m, 300 m and 1 km spatial resolution. At field level, depending on the spatial resolution and the NDVI threshold, the adjustedR²ranged from 0.20 to 0.74; jackknifed–leave-one-field-outcross validation–RMSE ranged from 0.6 to 1.07 t/ha and MAE ranged between 0.46 and 0.90 t/ha for thermal time analysis. The best results for yield estimation (adjustedR²= 0.74, RMSE =0.6 t/ha and MAE =0.46 t/ha)were obtained from the integration over thermal time of 100 m pixel resolution using a baseline NDVI threshold of 0.2 and without any selection based on pixel purity. The field scale yield estimation was aggregated to the regional scale using 56fields. At the regional level, there was a difference of 0.0012 t/ha between thermal and calendar time for average yield estimations. The standard error of mean results showed that the error was larger for a higher spatial resolution with no pixel purity and smaller when purity increased. These results suggest that, for winter wheat, a finer spatial resolution rather than a higher revisit frequency and an increasing pixel purity enable more accurate yield estimations when integrated over thermal time at the field scale and at the regional scale only if higher pixel purity levels are considered. This method can be extended to larger regions, other crops, and other regions in the world, although site and crop-specific adjustments will have to include other threshold temperatures to reflect the boundaries of phenological activity. In general, however, this methodological approach should be applicable to yield estimation at the parcel and regional scales across the world

Application of a topographic pedosequence in the Villány Hills for terroir characterization

Terroir refers to the geographical origin of wines. The landscape factors (topography, parent rock, soil, microbial life, climate, natural vegetation) are coupled with cultural factors (cultivation history and technology, cultivars and rootstock) and all together define a terroir. The physical factors can be well visualized by a slope profile developed into a pedosequence showing the regular configuration of the relevant physical factors for a wine district. In the present study the generalized topographic pedosequence (or catena) and GIS spatial model of the Villány Hills, a historical wine producing region, serves for the spatial representation and characterization of terroir types. A survey of properties of Cabernet Franc grape juice allowed the comparison of 10 vineyards in the Villány Wine District, Southwest Hungary. Five grape juice properties (FAN, NH3, YAN, density and glucose + fructose content) have been found to have a moderate linear relationship (0.5 0.7) with FAN, NH3, YAN, sugar and density and moderate correlation with primary amino nitrogen (PAN). HI showed a correlation with three nitrogen related parameters FAN, NH3, YAN, density and glucose + fructose content. Elevation and slope, however, did not correlate with any of the chemical properties

The CORDEX.be initiative as a foundation for climate services in Belgium

The CORDEX.be project created the foundations for Belgian climate services by producing high-resolution Belgian climate information that (a) incorporates the expertise of the different Belgian climate modeling groups and that (b) is consistent with the outcomes of the international CORDEX ("COordinated Regional Climate Downscaling Experiment") project. The key practical tasks for the project were the coordination of activities among different Belgian climate groups, fostering the links to specific international initiatives and the creation of a stakeholder dialogue. Scientifically, the CORDEX.be project contributed to the EURO-CORDEX project, created a small ensemble of High-Resolution (H-Res) future projections over Belgium at convection-permitting resolutions and coupled these to seven Local Impact Models. Several impact studies have been carried out. The project also addressed some aspects of climate change uncertainties. The interactions and feedback from the stakeholder dialogue led to different practical applications at the Belgian national level

Variability in the water footprint of arable crop production across European regions

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Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22 degrees resolution over the CORDEX Central Asia domain

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22 degrees (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980-2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temper-ature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified

Wheat yield estimation from NDVI and regional climate models in Latvia

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics