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Soil studies were carried out on 115,896.2 hectares of agricultural lands in fifteen villages of the municipal district obtained by subtracting from the available area of the village industrial lands, populated areas, forest plots occupied by water, etc.As a result of the land reform and redistribution of land for various purposes for the period from 1972 to 2021, the area of agricultural land decreased by 12.7% compared to the data of the previous survey.In the research area, the largest territories are occupied by black soils, which amount to 52,826.24 ha, including bleached soils—42,605.9 ha, alkaline – 6983.8 ha and shortened – 3236.54 ha. Slightly inferior to the black soils are dark gray forest soils with an area of 37,043.63 hectares, alluvial—12,287.4 hectares, gray forest—6371.96 hectares and forest soils of a rooted profile – 5058.94 hectares. The share of sod-carbonate soils accounts for 7792.7 hectares of land, which is 6.2%. The gradation did not include the soils of the ravine-beam complex, sand and gravel masses, existing ravines and disturbed lands, and quarries that occupy 5,452.4 hectares of territory (4.3%).One of the important indicators of soils, especially used in agricultural production, is the humus state. Thus, over 49 years there has been a slight decrease in the area under obese (high-humus) soils in the hectare ratio, due to a general decrease in the area of farmland, but in the context of the security group, they have increased by 1.3% (Table 1). The remaining levels of security have remained almost at the same level. The increase in the amount of fat chernozems was facilitated by the withdrawal of arable land from circulation and their transfer to perennial plantations. Earlier researches conducted on experimental fields of the Bashkir State Agrarian University identified and revealed changes in the quantitative and qualitative composition of organic matter from 15 to 30% when introducing a land plot for arable land26. To preserve and improve soil fertility, it is recommended to carry out a complex of agrotechnical, agrochemical and reclamation measures and the use of various meliorants, organic and mineral fertilizers27.Studies of the capacity of the humus horizon have shown that low–sized soils have become the most widespread—69,660.2 hectares or 60.1% of the total area of agricultural land (Fig. 2). A smaller area is occupied by medium-sized soils – 38,128.7 hectares (32.9%), not included in the gradation – 8107.3 hectares or 7.0%, respectively. It should be noted that the specific gravity of the soil of the ravine-beam complex, sand and gravel masses, active ravines and disturbed lands, and quarries increased by 2.5%.Figure 2Distribution of soils by humus horizon thickness by region.Full size imageThe granulometric composition of the soil is also of great agronomic importance28. Physical, physico-chemical, physico-mechanical properties and water, air, and nutrient regimes of soils depend on it29,30. In the Salavatskiy district there were practically no changes in soil areas in terms of granulometric composition, mainly clay soil varieties predominate. According to the mechanical composition of the soil there were distributed as follows: light clay – 71,807.38 ha or 62% (in 1972, 86,375 ha or 65.1%) of the total area of agricultural land and heavy loamy – 34,745.24 ha (30%) (in 1972—39,614 ha or 29.8%). The share of medium-loamy varieties accounts for 0.8% (in 1972—0.84%) (Fig. 3).Figure 3Distribution of Salavatskiy district soil areas by granulometric composition, %.Full size imageThe gradation did not include 8362.27 hectares of land. Heavy loamy, medium clay, sandy loam and sandy soils have not been identified.All arable soils of the analyzed territory are slightly susceptible to erosion processes, the processes of water and, to a lesser extent, wind erosion have developed. 67,445.21 hectares of land, or 58.2% (in 1972, 77,702 hectares) of the total area of agricultural lands are occupied under lightly washed soils, the share of medium and heavily washed accounts for 3.9% and 0.1%, respectively. Unwashed soils are distributed on 36,985.46 hectares (31.9%) (Table 2).Table 2 Soil areas by category of erosion feature (Salavatskiy district of the Republic of Bashkortostan).Full size tableAccording to the results of the field research and laboratory agrochemical analyses of soils, land refinements related to agricultural land were carried out. The basis for correcting and digitizing the contours of soil varieties were in the maps made in 1972 (Fig. 4).Figure 4Soil map within the boundaries of the Salavatskiy district of the Republic of Bashkortostan, 1972.Full size imageDigitization included scanning the topographic basis, then assigning coordinates to a raster image, decrypting and digitizing orthophotos (Fig. 5).Figure 5Orthophotoplan within the boundaries of the Salavatskiy district of the Republic of Bashkortostan, 2007.Full size imageAfter the carried-out activities, a soil map was obtained in the digital format of the Mapinfo program, after which it was converted into a raster basis with reference to the local coordinate system MSK 02 zone 1. The digitization of the 1972 soil map was carried out manually by outlining the contours of the topographic base and the scanned map.During digitization, information partially lost due to its wear and distortion during scanning was restored. A necessary condition is the use of the originals of the soil maps of the previous survey (1972).As a planned basis on which the created layers can be opened and information on soils can be obtained, a raster basis was ordinated into a local coordinate system (Fig. 6).Figure 6Completed soil map within the boundaries of the Salavat district of the Republic of Belarus, 2021.Full size imageThe result of the conducted research is the developed electronic digital soil map of the municipal district of Salavatskiy district which unites 15 rural settlements. The electronic soil map is presented in the form of a complex of electronic layers with the names of the type and subtype of soils, soil variety, mechanical or granulometric composition, soil-forming and underlying rocks. It also includes indicators of organic carbon, humus, mobile phosphorus, exchangeable potassium, soil acidity by pH value and the capacity of the humus-accumulative horizon. More

Direct injection method for ethylene and acetylene δ13C analyses by GC-C-IRMSFollowing the direct injection approach of classical ISARA12 with a few modifications, ARA samples with high ethylene yield ( > 500 ppmv) in 10% v/v acetylene were manually injected into a Thermo Scientific Trace GC Ultra-Isolink with an Agilent HP-PLOT/Q  capillary GC column (30 m, i.d. = 0.32 mm, f.t. = 20 μm) and a combustion reactor connected to a Thermo Scientific Delta V Plus isotope ratio mass spectrometer (GC-C-IRMS; Fig. 1a). Modifications include the replacement of silver ferrules in the GC oven with Valcon polymide (graphite reinforced polymer) ferrules to limit memory effects from acetylene. The combustion reactor was oxidized with pure oxygen for 1 h before each run and brief (15 min) seed oxidations were performed between measurement batches (i.e., required every ~ 6–8 ethylene injections, ~ 4–6 acetylene injections) to regenerate reactor oxidation capacity and minimize drift of δ13C values. See Supplementary Table S1a online for additional instrument settings.Ethylene Pre-Concentration (EPCon) methodARA samples with  More

Thermal requirements to develop PDWe examined the response of a wide spectrum of European grapevine varieties to XfPD infection in three independent experiments conducted in 2018, 2019, and 2020. Overall, 86.1% (n = 764) of 886 inoculated plants, comprising 36 varieties and 57 unique scion/rootstock combinations, developed PD symptoms 16 weeks after inoculation. European V. vinifera varieties exhibited significant differences in their susceptibility to XfPD (Supplementary Table S1). All varieties, however, showed PD symptoms to some extent, confirming previous field observations of general susceptibility to XfPD9,12,37. We also found significant differences in virulence (χ2 = 68.73, df = 1, P = 2.2 × 10−16) between two XfPD strains isolated from grapevines in Majorca across grapevine varieties (Supplementary Fig. S1). Full details on the results of the inoculation tests are available in “Methods”, Supplementary Note 1, Supplementary Table S1 and Supplementary Data 1.Growing degree days (GDD) have traditionally been used to describe and predict phenological events of plants and insect pests, but rarely in plant diseases58. We took advantage of data collated in the inoculation trials together with temperature to relate symptom development to the accumulated heat units at weeks eight, 10, 12, 14, and 16 after inoculation (Supplementary Data 1). Rather than counting GDDs linearly above a threshold temperature, we consider Xf ’s specific growth rate in vitro within its cardinal temperatures. The empirical growth rates come from the seminal work by Feil & Purcell38 shown in the inset of Fig. 1a. This Arrhenius plot was transformed, as explained in Supplementary Note 2A, to obtain a a piece-wise function f(T) Eq. (1). Our model and risk maps are based on f(T) (red line in Fig. 1a) because it provides the best fit to the experimental data when compared with the commonly used Beta function (blue line) for representing the thermal response in biological processes59,60. This Modified Growing Degree Day (MGDD) profile Eq. (1) enables to measure the thermal integral from hourly average temperatures, improving the prediction scale of the biological process61. MGDD also provides an excellent metric to link XfPD growth in culture with PD development as, once the pathogen is injected into the healthy vine, symptoms progression mainly depends upon the bacterial load (i.e., multiplication) and the movement through the xylem vessel network, which are fundamentally temperature-dependent processes38,62. Moreover, MGDD can be mathematically related to the exponential or logistic growth of the pathogen within the plant (Supplementary Note 2B).Fig. 1: Climatic and transmission layers composing the epidemiological model.a MGDD profile fitted to the in vitro data of Xf growth rate in Feil & Purcell 200138. The original Arrhenius plot in Kelvin degrees (inset) was converted to Celsius, as explained in (Supplementary Note 2A), to obtain the fit shown in the main plot red line; the blue line represents the fit with a Beta function. b Correlation between CDD and the average ({T}_{min }) of the coldest month between 1981 and 2019. Plotted black dots (worldwide) and yellow dots (main wine-producing zones) depict climatic data from 6,487,200 cells at 0.1∘ × 0.1∘ resolution, spread globally and retrieved from ERA5-Land dataset. The red solid line depicts the fitted exponential function for worldwide data and the blue solid line for main vineyard zones. c Nonlinear relationship between MGDD (red line) and CDD (blue line) and the likelihood of developing chronic infections. Black dots depict the cumulative proportion of grapevine plants in the population of 36 inoculated varieties showing five or more symptomatic leaves at each of the 15 MGDD levels (see Supplementary Information). Vertical bars are the 95% CI. d Combined ranges of MGDD and CDD on the likelihood of developing chronic infection. e Transmission layer in the dynamic equation (1) of the SIR compartmental model. f Relationship between the exponential growth of the number of infected plants with the risk index and their ranks.Full size imageInterannual infection survival in grapevines plays a relevant role when modelling PD epidemiology. In our model, we assumed a threshold of five or more symptomatic leaves for these chronic infections based on the relationship between the timing and severity of the infection during the growing season and the likelihood of winter recovery38,39,42. This five-leaf cut-off was grounded on: (i) the bimodal distribution in the frequency of the number of symptomatic leaves among the population of inoculated grapevines (Supplementary Fig. S1), whereby vines that generally show less than five symptomatic leaves at 12 weeks after inoculation remain so in the following weeks, while those that pass that threshold continue to produce symptomatic leaves, and (ii) the observed correlation between the acropetal and basipetal movement of Xf along the cane (Supplementary Fig. S1). The likelihood of developing chronic infections as a function of accumulated MGDD among the population of grapevine varieties was modelled using survival analysis with data fitted to a logistic distribution ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}})). A minimum window of MGDD = 528 was needed to develop chronic infections (var. Tempranillo), about 975 for a median estimate, while a cumulative MGDD  > 1159 indicate over 90% probability within a growing season (red curve in Fig. 1c and “Methods”).Next, we intended to model the probability of disease recovery by exposure to cold temperatures. Previous works had specifically modelled cold curing on Pinot Noir and Cabernet Sauvignon varieties in California as the effect of temperature and duration39 by assuming a progressive elimination of the bacterial load with cold temperatures42. In the absence of appropriate empirical data to formulate a general average pattern of winter curing among grapevine varieties, we combined the approach of Lieth et al.39 and the empirical observations of Purcell on the distribution of PD in the US related to the average minimum temperature of the coldest month, Tmin, isolines41. To consider the accumulation of cold units in an analogy of the MGDD, we searched for a general correlation between Tmin and the cold degree days (CDDs) with base temperature = 6 ∘C (see “Methods”). We found an exponential relation, ({{{{{rm{CDD}}}}}} sim 230exp (-0.26cdot {T}_{min })), where specifically, CDD ≳ 306 correspond to ({T}_{min } < -1.{1},^{circ }{{{{{rm{C}}}}}}) (Fig. 1b). To transform this exponential relationship to a probabilistic function analogous to ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}})), hereafter denoted ({{{{{{{mathcal{G}}}}}}}}({{{{{rm{CDD}}}}}})), ranging between 0 and 1, we considered the sigmoidal family of functions (f(x)=frac{A}{B+{x}^{C}}) with A = 9 × 106, B = A and C = 3 (Fig. 1c), fulfilling the limit ({{{{{{{mathcal{G}}}}}}}}({{{{{rm{CDD}}}}}}=0)=1), i.e., no winter curing when no cold accumulated, and a conservative 75% of the infected plants recovered at ({T}_{min }=-1.{1},^{circ }{{{{{rm{C}}}}}}) instead of 100% to reflect uncertainties on the effect of winter curing.MGDD/CDD distribution mapsMGDD were used to compute annual risk maps of developing PD during summer for the period 1981–2019 (see “Methods”). The resulting averaged map identifies all known areas with a recent history of severe PD in the US corresponding to ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}}) , > , 90 %) (i.e., high-risk), such as the Gulf Coast states (Texas, Alabama, Mississippi, Louisiana, Florida), Georgia and Southern California sites (e.g., Temecula Valley) (Fig. 2a), while captures areas with a steep gradation of disease endemicity in the north coast of California (({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}} , > , 50 % )). Overall, more than 95% of confirmed PD sites (n = 155) in the US (Supplementary Data 2) fall in grid cells with ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}}) , > , 50 %).Fig. 2: Average thermal-dependent maps for Pierce’s disease (PD) development and recovery in North America and Europe.PD development during the growing season based on average ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}})) estimations between 1981 and 2019 in North America (a) and Europe (b) derived from the results of the inoculation experiments on 36 grapevine varieties. Large differences in the areal extension with favourable MGDDs can be observed between the US and Europe. The winter curing effect is reflected in the distribution of the average ({{{{{{{mathcal{G}}}}}}}}({{{{{rm{CDD}}}}}})) for the 1981–2019 period in the United States (c) and Europe (d). A snapshot of the temperature-driven probability of chronic infection averaged for the 1981–2019 period is obtained from the joint effect of MGDD and CDD in North America (e) and Europe (f). Warmer colours indicate more favourable conditions for chronic PD and the dashed line highlights the threshold of chronic infection probability being 0.5.Full size imageThe average MGDD-projected map for Europe during 1981–2019 spots a high risk for the coast, islands and major river valleys of the Mediterranean Basin, southern Spain, the Atlantic coast from Gibraltar to Oporto, and continental areas of central and southeast Europe (Fig. 2b). Of these, however, only some Mediterranean islands, such as Cyprus and Crete, show ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}}) , > , 99 %) comparable to areas with high disease incidence in the Gulf Coast states of the US and California. Almost all the Atlantic coast from Oporto (Portugal) to Denmark are below suitable MGDD, with an important exception in the Garonne river basin in France (Bordeaux Area) with low to moderate MGDD (Fig. 2b).Figure 2a shows how the area with high-risk MGDD values extends further north of the current known PD distribution in the southeastern US, suggesting that winter temperatures limit the expansion of PD northwards9. A comparison between MGDD and CDD maps (Fig. 2a vs. Fig. 2c, Fig. 2e) further supports the idea that winter curing is restricting PD northward migration from the southeastern US. However, consistent with growing concern among Midwest states winegrowers on PD northward migration led by climate change63, we found a mean increase of 0.12% y−1 in the areal extent with CDD  0.075) in 22.3% of the vineyards in Europe. However, no vineyard is in epidemic-risk zones with a high-risk index and only 2.9% of the vineyard surface is at moderate risk (Supplementary Table S8). The areas with the highest risk index (r(t) between 0.70 and 0.88) are mainly located in the Mediterranean islands of Crete, Cyprus and the Balearic Islands or at pronounced peninsulas like Apulia (Italy) and Peloponnese (Greece) in the continent (Fig. 6a and Supplementary Table S8). Most vineyards are in non-risk zones (42.1%), whereas 35.6% are located in transition zones with presently non-risk but where XfPD could become established in the next decades causing some sporadic outbreaks. In Supplementary Data 4 and Supplementary Table S8, we provide full details of the total vineyard areas currently at risk for each country and region.Fig. 6: Intersection between Corine-land-cover vineyard distribution map and PD-risk maps for 2020 and 2050.Data were obtained from Corine-land-cover (2018) and the layer of climatic suitability forP. spumarius in Europe from35. The surface of the vineyard contour has been enlarged to improve the visualisation of the risk zones and disease-incidence growth-rate ranks. a PD risk map for 2019 and its projection for 2050 (b). Blue colours represent non-risk zones and transient risk zones for chronic PD (R0  More