The relationship between landscape patterns and human-caused fire occurrence in Spain

Aim of study: Human settlements and activities have completely modified landscape structure in the Mediterranean region. Vegetation patterns show the interactions between human activities and natural processes on the territory, and allow understanding historical ecological processes and socioeconomic factors. The arrangement of land uses in the rural landscape can be perceived as a proxy for human activities that often lead to the use, and escape, of fire, the most important disturbance in our forest landscapes. In this context, we tried to predict human-caused fire occurrence in a 5-year period by quantifying landscape patterns. Area of study: This study analyses the Spanish territory included in the Iberian Peninsula and Balearic Islands (497,166 km2). Material and Methods: We evaluated spatial pattern applying a set of commonly used landscape ecology metrics to landscape windows of 10x10 sq km (4751 units in the UTM grid) overlaid on the Forest Map of Spain, MFE200. Main results: The best logistic regression model obtained included Shannon’s Diversity Index, Mean Patch Edge and Mean Shape Index as explicative variables and the global percentage of correct predictions was 66.3 %. Research highlights: Our results suggested that the highest probability of fire occurrence at that time was associated with areas with a greater diversity of land uses and with more compact patches with fewer edges

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data

Assessing the grapevine crop water stress indicator over the flowering-veraison phase and the potential yield lose rate in important European wine regions

In Europe, most of vineyards are managed under rainfed conditions, where water deficit has become increasingly an issue. The flowering-veraison phenophase represents an important period for vine response to water stress, which is known to depend on variety characteristics, soil and climate conditions. In this paper, we have carried out a retrospective analysis for important European wine regions over 1986-2015, with objectives to assess the mean Crop Water Stress Indicator (CWSI) during flowering-veraison phase, and potential Yield Lose Rate (YLR) due to seasonal cumulative water stress. Moreover, we also investigate if advanced flowering-veraison phase can lead to alleviated CWSI under recent-past conditions, thus contributing to reduced YLR. A process-based grapevine model is employed, which has been extensively calibrated for simulating both flowering and veraison stages using location-specific observations representing 10 different varieties. Subsequently, grid-based modelling is implemented with gridded climate and soil datasets and calibrated phenology parameters. The findings suggest wine regions with higher mean CWSI of flowering-veraison phase tend to have higher potential YLR. However, contrasting patterns are found between wine regions in France-Germany-Luxembourg and Italy Portugal-Spain. The former tends to have slight-to-moderate drought conditions (CWSI0.5) and substantial YLR (>40%). Wine regions prone to a high drought risk (CWSI>0.75) are also identified, which are concentrated in southern Mediterranean Europe. Advanced flowering-veraison phase over 1986-2015, could have benefited from more spring precipitation and cooler temperatures for wine regions of Italy-Portugal-Spain, leading to reduced mean CWSI and YLR. For those of France-Germany-Luxembourg, this can have reduced flowering-veraison precipitation, but prevalent reductions of YLR are also found, possibly due to shifted phase towards a cooler growing-season with reduced evaporative demands. Our study demonstrates flowering-verasion water deficit is critical for potential yield, which can have different impacts between Central and Southern European wine regions. This phase can be advanced under a warmer climate, thus having important implications for European rainfed vineyards. The overall outcome may provide new insights for appropriate viticultural management of seasonal water deficits under climate change.This study was funded by Clim4Vitis project-"Climate change impact mitigation for European viticulture: knowledge transfer for an integrated approach", funded by the European Union's Horizon 2020 Research and Innovation Programme, under grant agreement no. 810176; it was also supported by FCT-Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020. We acknowledge the data provisions from members of the PEP725 project, from IPHEN project and from the Consejo Regulador of Ribera de Duero and Rioja DOCa

Use of Sentinel-2 Derived Vegetation Indices for Estimating fPAR in Olive Groves

Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where the increasing impact of climate change coupled with the inefficient management of olive groves is negatively affecting olive oil production and quality in some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing olive grove management and final production. However, the spatial resolution of freely-available satellite products is not yet adequate to estimate plant biophysical parameters in complex agroecosystems such as olive groves, where both olive trees and grass cover contribute to the vegetation indices (VIs) signal at pixel scale. The aim of this study is therefore to test a disentangling procedure to partition the VIs signal among the different components of the agroecosystem to use this information for the monitoring of olive growth processes during the season. Specifically, five VIs (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) as recorded by Sentinel-2 at a spatial resolution of 10 m over five olive groves in the Montalbano area (Tuscany, Central Italy), were tested as a proxy for olive tree intercepted radiation. The olive tree volume per pixel was initially used to linearly rescale the VIs signal into the relevant value for the grass cover and olive trees. The models, describing the relationship between rescaled VIs and observed fraction of Photosynthetically Active Radiation (fPAR), were fitted and then validated against independent datasets. While in the calibration phase, a greater robustness at predicting fPAR was obtained using NDVI (r = 0.96 and RRMSE = 9.86), the validation results demonstrating that GEMI and MCARI2/OSAVI provided the highest performances (GEMI: r = 0.89 and RRMSE = 21.71; MCARI2/OSAVI: r = 0.87 and RRMSE = 25.50), in contrast to MCARI2 that provided the lowest (r = 0.67 and RRMSE = 36.78). These results may be related to the VIs’ intrinsic features (e.g., lower sensitivity to atmosphere and background effects), which make some of these indices, compared to others, less sensitive to saturation effects by improving fPAR estimation (e.g., GEMI vs. NDVI). On this basis, this study evidenced the need to improve the current methodologies to reduce inter-row effects and select appropriate VIs for fPAR estimation, especially in complex agroecosystems where inter-row grass growth may affect remote sensed-derived VIs signal at an inadequate pixel resolution

Use of remote sensing‑derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub‑field level

Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level

Assessing climate change impacts on crops by adopting a set of crop performance indicators

AbstractThe impact of climate change on the agricultural systems of three major islands in the Mediterranean basin, namely Sicily, Crete and Cyprus, was evaluated using a suite of specifically calibrated crop models and the outputs of a regional circulation model for Representative Concentration Pathway (RCP) 4.5 and 8.5 downscaled to 12 km of resolution and tested for its effectiveness in reproducing the local meteorological data. The most important annual (wheat, barley, tomato and potato) and perennial (grapevine and olive tree) crops were selected to represent the agricultural systems of the islands. The same modelling framework was used to test the effectiveness of autonomous adaptation options, such as shifting sowing date and the use of varieties with different growing season length. The results highlighted that, on average, warmer temperatures advanced both anthesis and maturity of the selected crops, but at different magnitudes depending on the crop and the island. Winter crops (barley, wheat and potato) experienced the lowest impact in terms of yield loss with respect to the baseline, with even some positive effects, especially in Sicily where both wheat and barley showed a general increase of 9% as compared to the baseline, while potato increased up to + 17%. Amongst perennial crops, olive tree showed low variation under RCP 4.5, but on average increased by 7% under RCP 8.5 on the three islands. Climate change had a detrimental effect specifically on tomato (− 2% on average in RCP 8.5 and 4.5 on the three islands) and grapevine (− 7%). The use of different sowing dates, or different varieties, revealed that for winter crops early autumn sowing is still the best option for producing wheat and barley in future periods on the three islands under both future scenarios. For tomato and potato, advancing sowing date to early winter is a winning strategy that may even increase final yield (+ 9% for tomato and + 17% for potato, on average). For grapevine, the use of late varieties, while suffering the most from increasing temperatures and reduced rainfall (− 15%, on average), is still a valuable option to keep high yield levels with respect to earlier varieties, which even if showing some increases with respect to the baseline have a generally much lower production level. The same may be applied to olive tree although the production differences between late and early varieties are less evident and climate change exerts a favourable influence (+ 4 and + 3% for early and late varieties, respectively)

Agricultural Water Management

In Europe, most of vineyards are managed under rainfed conditions, where water deficit has become increasingly an issue. The flowering-veraison phenophase represents an important period for vine response to water stress, which is known to depend on variety characteristics, soil and climate conditions. In this paper, we have carried out a retrospective analysis for important European wine regions over 1986–2015, with objectives to assess the mean Crop Water Stress Indicator (CWSI) during flowering-veraison phase, and potential Yield Lose Rate (YLR) due to seasonal cumulative water stress. Moreover, we also investigate if advanced flowering-veraison phase can lead to alleviated CWSI under recent-past conditions, thus contributing to reduced YLR. A process-based grapevine model is employed, which has been extensively calibrated for simulating both flowering and veraison stages using location-specific observations representing 10 different varieties. Subsequently, grid-based modelling is implemented with gridded climate and soil datasets and calibrated phenology parameters. The findings suggest wine regions with higher mean CWSI of flowering-veraison phase tend to have higher potential YLR. However, contrasting patterns are found between wine regions in France-Germany-Luxembourg and Italy-Portugal-Spain. The former tends to have slight-to-moderate drought conditions (CWSI0.5) and substantial YLR (>40%). Wine regions prone to a high drought risk (CWSI>0.75) are also identified, which are concentrated in southern Mediterranean Europe. Advanced flowering-veraison phase over 1986–2015, could have benefited from more spring precipitation and cooler temperatures for wine regions of Italy-Portugal-Spain, leading to reduced mean CWSI and YLR. For those of France-Germany-Luxembourg, this can have reduced flowering-veraison precipitation, but prevalent reductions of YLR are also found, possibly due to shifted phase towards a cooler growing-season with reduced evaporative demands. Our study demonstrates flowering-verasion water deficit is critical for potential yield, which can have different impacts between Central and Southern European wine regions. This phase can be advanced under a warmer climate, thus having important implications for European rainfed vineyards. The overall outcome may provide new insights for appropriate viticultural management of seasonal water deficits under climate change