Modeling fire ignition probability and frequency using Hurdle models: a crossregional study in Southern Europe

Background:Wildfires play a key role in shaping Mediterranean landscapes and ecosystems and in impacting species dynamics.Numerous studies have investigated the wildfire occurrences and the influence of their drivers in many countries of the Mediterranean Basin.However,in this regard,no studies have attempted to compare different Mediterranean regions,which may appear similar under many aspects.In response to this gap,climatic,topographic,anthropic,and landscape drivers were analyzed and compared to assess the patterns of fire ignition points in terms of fire occurrence and frequency in Catalonia(Spain),Sardinia,and Apulia(Italy).Therefore,the objectives of the study were to(1)assess fire ignition occurrence in terms of probability and frequency,(2)compare the main drivers affecting fire occurrence,and(3)produce fire probability and frequency maps for each region.Results:In pursuit of the above,the probability of fire ignition occurrence and frequency was mapped using Negative Binomial Hurdle models,while the models’performances were evaluated using several metrics(AUC,prediction accuracy,RMSE,and the Pearson correlation coefficient).The results showed an inverse correlation between distance from infrastructures(i.e.,urban roads and areas)and the occurrence of fires in all three study regions.This relationship became more significant when the frequency of fire ignition points was assessed.Moreover,a positive correlation was found between fire occurrence and landscape drivers according to region.The land cover classes more significantly affected were forest,agriculture,and grassland for Catalonia,Sardinia,and Apulia,respectively.Conclusions:Compared to the climatic,topographic,and landscape drivers,anthropic activity significantly influences fire ignition and frequency in all three regions.When the distance from urban roads and areas decreases,the probability of fire ignition occurrence and frequency increases.Consequently,it is essential to implement long-to medium-term intervention plans to reduce the proximity between potential ignition points and fuels.In this perspective,the present study provides an applicable decision-making tool to improve wildfire prevention strategies at the European level in an area like the Mediterranean Basin where a profuse number of wildfires take place.

Yield models for predicting aboveground ectomycorrhizal fungal productivity in Pinus sylvestris and Pinus pinaster stands of northern Spain

Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, the currently existing models are based on rather local data and, thus, there is a lack of predictive tools to monitor mushroom yields on larger scales.Results: This work presents the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms and related ecosystem services in Pinus sylvestris and Pinus pinaster stands in northern Spain, using a long-term dataset suitable to account for the combined effect of meteorological conditions and stand structure.Models were fitted for the following groups of fungi separately: all ectomycorrhizal mushrooms, edible mushrooms and marketed mushrooms. Our results show the influence of the weather variables(mainly precipitation) on mushroom yields as well as the relevance of the basal area of the forest stand that follows a right-skewed unimodal curve with maximum predicted yields at stand basal areas of 30–40 m2·ha-1.Conclusion: These models are the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms in Pinus sylvestris and Pinus pinaster stands in northern Spain, being of the highest resolution developed to date and enable predictions of mushrooms productivity by taking into account weather conditions and forests’ location, composition and structure.

Modelling tree mortality across diameter classes using mixedeffects zero-inflated models

The mortality of trees across diameter class model is a useful tool for predicting changes in stand structure.Mortality data commonly contain a large fraction of zeros and general discrete models thus show more errors.Based on the traditional Poisson model and the negative binomial model,different forms of zero-inflated and hurdle models were applied to spruce-fir mixed forests data to simulate the number of dead trees.By comparing the residuals and Vuong test statistics,the zero-inflated negative binomial model performed best.A random effect was added to improve the model accuracy;however,the mixed-effects zero-inflated model did not show increased advantages.According to the model principle,the zeroinflated negative binomial model was the most suitable,indicating that the"0"events in this study,mainly from the sample"0",i.e.,the zero mortality data,are largely due to the limitations of the experimental design and sample selection.These results also show that the number of dead trees in the diameter class is positively correlated with the number of trees in that class and the mean stand diameter,and inversely related to class size,and slope and aspect of the site.

Adoption and Impact of Modern Rice Varieties on Poverty in Eastern India

The factors affecting the adoption of modern varieties(MVs) of rice and impact on poverty in Odisha, India were discussed. A total of 363 households from Cuttack and Sambalpur districts of Odisha via multistage sampling technique participated in the survey. The Cragg's Double hurdle model was used to model the determinants of adoption and intensity of adoption of MVs of rice, and the propensity score matching was used to analyze the impact of adoption on poverty. The results showed that age, education, risk aversion, land size, yield, perception of MVs as high yielding, resistant to diseases and availability of MVs positively influenced the decision to adopt. However, variables such as household size, experience of a farmer, off-farm job participation, amount of credit received, cost of seeds, insecticides and fertilizers negatively influenced the adoption of MVs. Intensity of adoption of MVs was negatively influenced by experience of a farmer, cost of fertilizer and marketability of MVs, and positively affected by household size, risk aversion, land size, cost of insecticides, perception of MVs as high yielding and availability of MV seeds. Poverty incidence, gap and severity were high among non-adopters to adopters of MVs. After matching adopters and non-adopters of MV groups using four different algorithms of nearest neighbour matching, stratification matching, radius matching and kernel matching, the impact of MV adoption resulted in higher per capita monthly household expenditure by about US$ 52.82 to US$ 63.17.

Influencing factors of manufacturing agglomeration in the Beijing-Tianjin-Hebei region based on enterprise big data

Industrial agglomeration is a highly prominent geographical feature of economic activities,and it is an important research topic in economic geography.However,mechanism-based explanations of industrial agglomeration often differ due to a failure to distinguish properly between the spatial distribution of industries and the stages of industrial agglomeration.Based on micro data from three national economic censuses,this study uses the Duranton-Overman(DO)index method to calculate the spatial distribution of manufacturing industries(three-digit classifications)in the Beijing-Tianjin-Hebei region(BTH region hereafter)from 2004 to 2013 as well as the hurdle model to explain quantitatively the influencing factors and differences in the two stages of agglomeration formation and agglomeration development.The research results show the following:(1)In 2004,2008,and 2013,there were 124,127,and 129 agglomerations of three-digit industry types in the BTH region,respectively.Technology-intensive and labor-intensive manufacturing industries had high agglomeration intensity,but overall agglomeration intensity declined during the study period,from 0.332 to 0.261.(2)There are two stages of manufacturing agglomeration,with different dominant factors.During the agglomeration formation stage,the main locational considerations of enterprises are basic conditions.Agricultural resources and transportation have negative effects on agglomeration formation,while labor pool and foreign investment have positive effects.In the agglomeration development stage,enterprises focus more on factors such as agglomeration economies and policies.Internal and external industry linkages both have a positive effect,with the former having a stronger effect,while development zone policies and electricity,gas,and water resources have a negative effect.(3)Influencing factors on industrial agglomeration have a scale effect,and they all show a weakening trend as distance increases,but different factors respond differently to distance.

Schedule Compression Impact on Construction Project Safety

Many construction projects are met with stringent timelines or the threat of exorbitant liquidated damages. In addition, construction schedulers are frequently forced to incorporate aggressive schedule compression techniques. As already discussed by previous researchers, these schedule compression techniques have direct impacts on project productivity and quality defects.Researchers have also pointed out that schedule compression will affect safety incidents such as Occupational Safety & Health Administration recordable injuries and near misses over long project durations. However, most of the existing studies treated safety as a subcategory of project productivity and project quality, and minimal research has been done to directly quantify the effect of schedule compression on safety at the project level.Therefore, in this research, we conducted a survey and statistical analysis to investigate the relationship between schedule compression and safety in construction projects.We interviewed various members of the Houston construction community from both industrial and non-industrial roles. Statistical analysis was used to identify factors that have significant impacts on the occurrence of safety incidents at an industry specific level.