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)

Modelling different cropping systems

Grapevine is a worldwide valuable crop characterized by a high economic importance for the production of high quality wines. However, the impact of climate change on the narrow climate niches in which grapevine is currently cultivated constitute a great risk for future suitability of grapevine. In this context, grape simulation models are considered promising tools for their contribution to investigate plant behavior in different environments. In this study, six models developed for simulating grapevine growth and development were tested by focusing on their performances in simulating main grapevine processes under two calibration levels: minimum and full calibration. This would help to evaluate major limitations/strength points of these models, especially in the view of their application to climate change impact and adaptation assessments. Preliminary results from two models (GrapeModel and STICS) showed contrasting abilities in reproducing the observed data depending on the site, the year and the target variable considered. These results suggest that a limited dataset for model calibration would lead to poor simulation outputs. However, a more complete interpretation and detailed analysis of the results will be provided when considering the other models simulations

Stochastic smoothing of point processes for wildlife-vehicle collisions on road networks

Wildlife-vehicle collisions on road networks represent a natural problem between human populations and the environment, that affects wildlife management and raise a risk to the life and safety of car drivers. We propose a statistically principled method for kernel smoothing of point pattern data on a linear network when the first-order intensity depends on covariates. In particular, we present a consistent kernel estimator for the first-order intensity function that uses a convenient relationship between the intensity and the density of events location over the network, which also exploits the theoretical relationship between the original point process on the network and its transformed process through the covariate. We derive the asymptotic bias and variance of the estimator, and adapt some data-driven bandwidth selectors to estimate the optimal bandwidth. The performance of the estimator is analysed through a simulation study under inhomogeneous scenarios. We present a real data analysis on wildlife-vehicle collisions in a region of North-East of Spain

Modelling different cropping systems

Grapevine is a worldwide valuable crop characterized by a high economic importance for the production of high quality wines. However, the impact of climate change on the narrow climate niches in which grapevine is currently cultivated constitute a great risk for future suitability of grapevine. In this context, grape simulation models are considered promising tools for their contribution to investigate plant behavior in different environments. In this study, six models developed for simulating grapevine growth and development were tested by focusing on their performances in simulating main grapevine processes under two calibration levels: minimum and full calibration. This would help to evaluate major limitations/strength points of these models, especially in the view of their application to climate change impact and adaptation assessments. Preliminary results from two models (GrapeModel and STICS) showed contrasting abilities in reproducing the observed data depending on the site, the year and the target variable considered. These results suggest that a limited dataset for model calibration would lead to poor simulation outputs. However, a more complete interpretation and detailed analysis of the results will be provided when considering the other models simulations