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Knotted1-like homeobox (KNOX) transcription factors are involved in plant development, playing complex roles in aerial organs. As Prunus species include important fruit tree crops of Italy, an exhaustive investigation of KNOX genes was performed using genomic and RNA-seq meta-analyses. Micropropagation is an essential technology for rootstock multiplication; hence, we investigated KNOX transcriptional behavior upon increasing 6-benzylaminopurine (BA) doses and the effects on GF677 propagules. Moreover, gene function in Prunus spp. was assessed by Gisela 6 rootstock transformation using fluorescence and peach KNOX transgenes. Based on ten Prunus spp., KNOX proteins fit into I-II-M classes named after Arabidopsis. Gene number, class member distribution, and chromosome positions were maintained, and exceptions supported the diversification of Prunus from Cerasus subgenera, and that of Armeniaca from the other sections within Prunus. Cytokinin (CK) cis-elements occurred in peach and almond KNOX promoters, suggesting a BA regulatory role in GF677 shoot multiplication as confirmed by KNOX expression variation dependent on dose, time, and interaction. The tripled BA concentration exacerbated stress, altered CK perception genes, and modified KNOX transcriptions, which are proposed to concur in in vitro anomalies. Finally, Gisela 6 transformation efficiency varied (2.6–0.6%) with the genetic construct, with 35S:GFP being more stable than 35S:KNOPE1 lines, which showed leaf modification typical of KNOX overexpression. [ABSTRACT FROM AUTHOR]

Olive agro-ecosystems in southern Italy have been heavily damaged due to Xylella fastidiosa subsp. pauca (Xfp). Replacing the Xfp-infected olive-growing areas with economically viable fruit tree species is thought to be a practical control measure. A land suitability analysis can provide an appropriate evaluation of a crop's suitability in these areas. We evaluate the suitability of almond (Prunus dulcis B.), fig (Ficus carica L.), hazelnut (Corylus avellana L.), kiwifruit (Actinidia chinensis P.), pistachio (Pistacia vera L.), and pomegranate (Punica granatum L.) as fruit tree species immune/resistant to Xfp to be planted within the Xfp-infected olive-growing areas in the Apulia region to compensate for economic and environmental losses. Climate and soil data were used to carry out the land suitability analysis. We combined information for each parameter to obtain the overall suitability maps for the six proposed fruit tree crops using GIS (Geographic Information System). The analysis showed that the Xfp-infected olive-growing areas are suitable for the plantation of most of the proposed fruit tree crops, with different suitability levels as the climate and soil conditions vary among the study areas. In particular, large olive-growing areas are suitable for the cultivation of pomegranate (268,886 ha), fig (103,975 ha), and almond (70,537 ha), followed by kiwifruit (43,018 ha) and pistachio (40,583 ha). Hazelnut, with just 2744 ha of suitable land, was the species with fewer suitable areas in these semi-arid environments. This is the first study to provide practical containment measures against the diffusion of Xfp in southern Italy. Our results can help in the selection of the right immune/resistant tree species for replanting in Xfp-infected zones, therefore providing guidelines within the decision-making process to encourage the planting of some underrepresented fruit tree crops with viable economic values as well. [ABSTRACT FROM AUTHOR]

The brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), native to eastern Asia, has become one of the most serious pests of fruit orchards worldwide. This invasive species is highly polyphagous and capable of moving across the agricultural matrix at long distances. However, comprehensive studies exploring how landscape characteristics influence H. halys colonization of orchards are still lacking. Here, we investigated the impact of landscape composition on the trap captures of H. halys in 113 orchards of different fruit tree crops (i.e., apple, pear, peach, walnut and kiwi) in northern Italy. We found that landscapes rich in semi-natural habitats and vineyards and poor in annual crops supported a higher abundance of H. halys juveniles in traps. This pattern became more evident towards the end of the growing season as the abundance of juveniles increased. Adults were instead not affected by the landscape. The invasive stink bug best responded to landscape processes at large spatial scales (3000 m) confirming its high dispersal ability. Moreover, H. halys did not display a strong preference among fruit orchards, although fewer individuals were caught in walnut orchards. Our findings suggest that the habitat composition of agricultural landscapes is a key factor driving the dynamics of this pest in agroecosystems and that semi-natural habitats might be important in supporting H. halys populations and crop colonization. These effects are however limited to juveniles while adult density was similar even in landscapes with very contrasting structures. • We studied the impact of landscape composition on H. halys populations in 113 orchards. • Landscapes rich in seminatural habitats and vineyards supported higher abundance of H. halys juveniles. • Adults were not affected by the landscape. • Landscapes composition is a key factor driving the dynamics of H. halys in agroecosystems. [ABSTRACT FROM AUTHOR]

The cultivation of European hazelnut (Corylus avellana L.) is showing a rapid increase due to the high global demand for hazelnuts from the confectionery industry also affecting Italy, which is the second largest hazelnut producing country after Turkey. Like most tree crops, hazelnut tree planting includes land preparation and frequent tillage operations during the first years of growing seasons to avoid weed competition, whereas after 3 years the soils are usually permanently grass covered. The aim of this work is to develop a model of carbon sequestration based on the age of hazelnut orchards, using permanent grasslands as reference for previous land use. The results demonstrate that soil carbon stock (sCS 30) tends to decrease between 23% and 58% during the first 3–5 years after cultivation. This carbon depletion is only partially compensated by the carbon sequestration of the hazelnut trees, variable from about 0.6–3.3 Mg C ha−1. Adult hazelnut orchards showed high levels of sCS 30 , as well as higher carbon stock (CS) from tree biomass, resulting in similar, or even higher carbon sequestration potential of the grassland. In some cases, excessive nitrogen fertilization seems to decrease the carbon sequestration potential of the soil. In conclusion, the land use change from grassland to hazelnut cultivation has a short-term negative impact in terms of soil carbon sequestration. In orchards with optimal fertilization and management, sCS 30 is recovered and exponentially increased, highlighting the high carbon sequestration potential offered by this kind of perennial nut crop. [Display omitted] • Hazelnut tree cultivation is strongly increasing in Mediterranean and temperate climate. • Grasslands to hazelnut conversion have negative effects on SOC during the first 3–5 years. • Adult hazelnut orchards sequester much organic carbon, both in soil and in tree. • Hazelnut orchards older than 35 years usually show higher soil carbon stock than grassland. • Nitrogen fertilization seems to affect the soil carbon storage potential. [ABSTRACT FROM AUTHOR]

Hazelnut is an emerging tree crop in the global market and current production levels are unable to sustain the demand for fruits. Research efforts are needed to support private and public stakeholders with knowledge that can help improving the management of the agricultural systems, by enhancing yield levels and safeguarding environmental sustainability. This paper extends the application of a process-based hazelnut simulation model from the single orchard where it was initially calibrated to four orchards located in Italy, Chile and Georgia, using three-year experimental datasets (2015–2017). The model sensitivity to parameter changes was assessed twice (screening and quantitative analysis) to identify the most relevant parameters affecting the prediction of leaf area index, fruit yield and soil moisture. These parameters were automatically calibrated to maximize the model accuracy in predicting field data, by comparing model performances with original parameterization. According to current model structure, canopy expansion and plant dimensions accounted for most of the variability in leaf area index simulation. Photosynthesis and root water uptake played a prominent role in affecting the model predictions of yield and soil moisture, respectively. The automatic calibration increased the accuracy in predicting leaf area index (average RRMSE = 19.3%), fruit yield (average RRMSE = 16.3%) and soil moisture (average RRMSE = 12.5%). This study provides quantitative information to support calibration activities and insights for collection of new experimental data in the short-term, while laying the groundwork for the use of the model for yield forecasting activities as the medium goal. • A hazelnut simulation model is applied on four orchards in Chile, Italy and Georgia. • The sensitivity analysis identifies the key parameters affecting model accuracy. • The model is calibrated with experimental data to maximize its accuracy. • The methodological workflow is reusable for further operational use of the model. [ABSTRACT FROM AUTHOR]

Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products. [ABSTRACT FROM AUTHOR]

• A methodology for estimating regional soil organic carbon content has been developed. • A national SOC estimate for forest and agro-ecosystems in Italy is provided. • Soil organic carbon content under climate change conditions has been calculated. • Climate change causes a moderate soil carbon loss for forested and tree crop soils. The soil organic carbon (SOC) is the largest carbon pool in the terrestrial biosphere, second only to oceans, containing twice as much carbon as the atmosphere and three times that stored in global vegetation. Climate change (CC) is expected to impact this carbon pool. To date, large uncertainties still persist on the effects of CC on SOC stocks. In addition, a shortage of data related to regional SOC quantities of tree-covered areas and future changes under CC conditions is recognized. In this work, we used a spatial-explicit modelling approach to estimate the current SOC stock (at 2005) and future changes (at 2095) under CC conditions of the whole forest, tree crop, grassland, and pasture covered areas of Italy. A methodology was preliminarily implemented to obtain spatialized SOC estimates at a regional scale by using the CENTURY 5 model coupled with spatialized vegetation, soil, and climate data. We ran both moderate (RCP4.5) and extreme (RCP8.5) climatic scenarios, and used three Global Circulation Models for each one of the four ecosystems described above. The current SOC stock estimates range from 51.3 (orchards) to 129.5 Mg carbon ha−1 (coniferous forests) and we found an overall SOC stock in Italy ranging from 1320.1 to 1425.1 Tg. SOC projections under CC showed a moderate carbon loss suggesting that forest, grassland, and permanent crop soils could provide an important contribution to climate change mitigation. [ABSTRACT FROM AUTHOR]