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Although a large part of Italy is characterized by a Mediterranean climate intrinsically highly suitable for olive cultivation, farmers may experience variable interannual yields with associated agronomic and management costs. A detailed overview of major climate stressors and their ongoing impacts on olive yield variability at a broad spatial–temporal scale across Italy could improve the understanding of how interannual olive yields are modulated by seasonal local climate and would enhance the development of actionable services to alert stakeholders of potential climate risks. We analysed aggregated olive yield data from the Italian National Statistics Institute (ISTAT) at the provincial level during 2006–2020, and several climatic variables from the Reanalysis v5 (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) as a basis to (i) explore olive yield trends and inter‐annual variations over the whole country; (ii) identify major seasonal climate stressors likely responsible for the largest variations in yield and (iii) develop a composite index that summarizes the risk of having exceptionally low yields due to the co‐occurrence of multiple stressors. To this end, we defined two extreme classes of yield, namely, exceptionally low and high yields (LY and HY, respectively) and explored the climatic variables aggregated on a bimonthly time scale that influenced yield outcomes. Our analysis showed that exceptional low yields have been erratically increasing since 2014 with temperature‐related variables having the highest impact, especially a relatively warm winter and cool early autumn. These period‐specific variables were major components of the resulting composite index predicting the likelihood of LY ranging from 28% to 49% due to increasing stress effects. Possible explanations of our findings are discussed, including the proliferation of the olive fly. We suggest the composite risk approach could lay the groundwork for an integrated meteorological seasonal forecasting system that provides timely insights on factors affecting within‐season olive yield development. [ABSTRACT FROM AUTHOR]

BACKGROUND: A likely increasing demand for varieties mixtures, landraces and genetic diversity in cropping systems will underpin calls for models able to generalize phenological development at the species level, at the same time as providing the expected range of phenological variability. In the present article, we aimed to obtain a generalized phenological model of durum wheat (Triticum durum, Desf.). RESULTS: Using a large phenological dataset embracing field data collected under different sowing dates, varieties and locations over the Italian peninsula, we searched for the phenophases enabling the best linear approximations between developmental rates and air temperature, aiming to minimize the residual variability from drivers other than temperature, as genetic and environmental diversity. The developmental rates of the resulting phases were then examined with respect to the mean daylength to determine possible additional relations with photoperiod. If a correlation with daylength was also present, the developmental rate is calibrated by multiple linear regression, or otherwise by simple linear regression of temperature. The resulting calibration, tested on an independent data subset, confirms that the model is able to generalize wheat development over the Italian peninsula with high accuracy (mean absolute error =3–8 days; r2 = 0.75–0.98), regardless of the wheat variety. CONCLUSION: The generalized phenological model is potentially suitable for many agro‐ecological and large‐scale applications. It is hoped that the model will aid in situations where phenological observations to parameterize a model are still lacking, as is probably the case for landraces and underutilized crop varieties. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

In a previous work we developed a mathematical model to explain the co-occurrence of evergreen and deciduous oak groups in the Mediterranean region, regarded as one of the distinctive features of Mediterranean biodiversity. The mathematical analysis showed that a stabilizing mechanism resulting from niche difference (i.e. different water use and water stress tolerance) between groups allows their coexistence at intermediate values of suitable soil water content. A simple formal derivation of the model expresses this hypothesis in a testable form linked uniquely to the actual evapotranspiration of forests community. In the present work we ascertain whether this simplified conclusion possesses some degree of explanatory power by comparing available data on oaks distributions and remotely sensed evapotranspiration (MODIS product) in a large-scale survey embracing the western Mediterranean area. Our findings confirmed the basic assumptions of model addressed on large scale, but also revealed asymmetric responses to water use and water stress tolerance between evergreen and deciduous oaks that should be taken into account to increase the understating of species interactions and, ultimately, improve the modeling capacity to explain co-occurrence. [ABSTRACT FROM AUTHOR]

The scientific community offers numerous crop models with different levels of sophistication. In such a wide range of crop models, users should have the possibility to choose the most suitable, in terms of detail, scale and representativeness, to their objectives. However, even when an appropriate choice is made, model limitations should be clarified such that modelling studies are put in the proper perspective and robust applications are achieved. This work is an overview of available models to simulate crop growth and yield. A summary matrix with more than 70 crop models is provided, storing the main model characteristics that can help users to choose the proper tool according to their purposes. Overall, we found that two main aspects of models, despite their importance, are not always clear from the published references, i.e. the versatility of the models, in terms of reliable transferability to different conditions, and the degree of complexity. Hence, the developers of models should be encouraged to pay more attention to clarifying the model limitations and limits of applicability, and users should make an effort in proper model selection, to save time often devoted to iteration of tuning steps to force an inappropriate model to be adapted to their own purpose. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Tropical deforestation is well known to have serious negative consequences for biodiversity, terrestrial carbon sinks and the balance of atmospheric greenhouse gases. By contrast, selective logging of tropical forests is often regarded as having a lesser impact on the ecosystem particularly in long terms, even though there have been few critical evaluations of the practice, particularly in Africa. We compared field data from 511 plots in the tropical forest of Sierra Leone, Ghana, Cameroon and Gabon. These plots were subject to different forest management practices: no recent logging (primary forests), selective logging (up to 30 years old) and re-grown secondary forests post clear-cutting (at least 20 years ago). Our findings suggest that the vertical structure and plant richness of the selectively logged and secondary forests change in different amplitude from those of primary forests, but stem density and the prevalence of vine and weed species differ greatly. We show that the effects of selective logging are greater than those expected simply from the removal of commercial species, and can persist for decades. Selective logging, unless it is practiced at very low harvest intensities, can significantly reduce the biomass of a tropical forest for many decades, seriously diminishing aboveground carbon storage capacity, and create opportunities for weeds and vines to spread and slow down the ecological succession. [ABSTRACT FROM AUTHOR]