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Russia has been reporting almost no changes in forested area, growing stock volume (GSV) and biomass to the United Nations Framework Convention on Climate Change (UNFCCC)1 and the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA)2 since the collapse of the USSR and the decline in the Soviet Forest Inventory and Planning (FIP) system. According to the State Forest Register (SFR)3, which is the main repository of forest information, and national reporting to the FAO FRA2, the GSV and the above ground biomass (AGB) increased by 1.1% and 0.6% (Table S1), respectively, during 1990–2015, yet studies using remote sensing (RS) indicate increased vegetation productivity4, tree cover (annual rate + 0.417% over 1982–2016)5, increased AGB (+ 329 Tg C yr−1 over 2000–20076), total biomass (annual rate + 0.44% or + 153 Tg C yr−1 over 1990–20077), and forest ecosystem carbon pools (ca + 470 Tg C yr−1 over 2001–20198). This inconsistency in estimates can be explained by an information gap that appeared when Russia decided to move from the FIP to another system for the collection of forest information at the national scale – the National Forest Inventory (NFI).The FIP involves revisiting every forest stand (on the ground for managed forests or using RS techniques for remote non-commercial forests) on a 10–15-year interval, with the measurement of forest parameters combined with the formulation of forest management directives. After the collapse of the USSR, the inventory within the FIP system slowed down substantially. For example, more than 50% of the forest area was surveyed by the FIP more than 25 years ago9. For these reasons, the reliability of information on forests in Russia has deteriorated since 1988, which is the year when FIP-based reporting10 involved the largest inventory efforts in recent decades. According to this report10, the total GSV of Russian forests was 81.7 × 109 m3 (without shrubland, bias corrected11). This value is used here as a reference to quantify biomass stock changes in Russia with respect to the current decade.In contrast, NFI is a state-of-the-art inventory system based on a statistical sampling method. It was initiated in 2007 and the first cycle was completed in 2020. The NFI data processing is ongoing, but the first official press release12 suggests that Russian forest accumulated 102 × 109 m3 over its lifespan until 2014. Once finalized, the NFI will be verified before adoption as the official source of information to the SFR and national reporting. The NFI has received some criticism13 because of the relatively sparse sampling employed and the stratification method used, which is partially based on outdated FIP data.In Russia, the long intervals between consecutive surveys and the difficulty in accessing very remote regions in a timely manner by an inventory system make satellite RS an essential tool for capturing forest dynamics and providing a comprehensive, wall-to-wall perspective on biomass distribution. However, observations from current RS sensors are not suited for producing accurate biomass estimates unless the estimation method is calibrated with a dense network of measurements from ground surveys14. Here we calibrated models relating two global RS biomass data products (GlobBiomass GSV15 and CCI Biomass GSV16) and additional RS data layers (forest cover mask9, the Copernicus Global Land Cover CGLS‐LC100 product17) with ca 10,000 ground plots (see Material and Methods) to reduce nuances in the individual input maps due to imperfections in the RS data and approximations in the retrieval procedure18,19. The combination of these two sources of information, i.e., ground measurements and RS, utilizes the advantages of both sources in terms of: (i) highly accurate ground measurements and (ii) the spatially comprehensive coverage of RS products and methods. The amount of ground plots currently available may be insufficient for providing an accurate estimate of GSV for the country when used alone, but they are the key to obtaining unbiased estimates when used to calibrate RS datasets20. The map merging procedure was preferred over a plot-aided direct estimation of GSV or AGB from the RS data because of the usually poor association between biomass measured at inventory plots and remote sensing observables21. In addition, models relating biomass and remote sensing observables that are trained with spatially inhomogeneous datasets (Figure S1) tend to be biased in regions not represented by the dataset of the reference biomass measurements.We estimate the total GSV of Russia for the year 2014 for the official forested area (713.1 × 106 ha) to be 111 ± 1.3 × 109 m3, which is 39% higher than the 79.9 × 109 m3 (excluding shrubland) figure reported in the SFR3 for the same year. An additional 7.1 × 109 m3 or 9% were found due to the larger forested area (+ 45.7 106 ha) recognized by RS9, following the expansion of forests to the north22, to higher elevations, in abandoned arable land23, as well as the inclusion of parks, gardens and other trees outside of forest, which were not counted as forest in the SFR. Based on cross-validation, our estimate at the regional level (81 regions of Russia – Table S2, Figure S2) is unbiased. The standard error varied from 0.6 to 17.6% depending on the region. The median error was 1.6%, while the area weighted error was 1.2%. The predicted GSV (Fig. 1) with associated uncertainties is available here (https://doi.org/10.5281/zenodo.3981198) as a GeoTiff at a spatial resolution of 3.2 arc sec. (ca 0.5 ha).Figure 1Predicted mean forest growing stock volume (m3 ha-1) for the year ca 2014 (Generated by Esri ArcGIS Desktop v.10.7, URL: https://desktop.arcgis.com/en/arcmap/).Full size imageHoughton et al.24 estimated forest biomass based on RS and FIP data in Russia for the year 2000. Average forest biomass density varied between 80.6 and 88.2 Mg ha-1 depending on which forest mask was used. Our estimate for the year 2014 of 107 Mg ha-1 (using the conversion factor of GSV to AGB from24 0.6859) is 21–33% higher than the one by Houghton et al., but this is consistent with expected biomass increases over time, i.e., 14 years after the Houghton et al. estimate.Assuming an unchanged total forest area (721.7 × 106 ha) in 1988 and 2014, we conclude that Russian forests have accumulated 1,163 × 106 m3 yr-1 or 407 Tg C yr-1 in live biomass of trees on average over 26 years. This gives an average GSV change rate of + 1.61 m3 ha-1 yr-1 or + 0.56 t C ha-1 yr-1. The sequestration rate obtained, however, should be treated with caution because different methods have been applied in 1988 and 2014 (see “Caveats and Limitations” section). To provide some context for the magnitude of these numbers, one can compare the Russian forest gain to the net GSV losses in tropical forests over the period 1990–2015 according to FAO FRA25 (-1,033 × 106 m3 yr-1 in the regions with a negative trend: South and Central America, South and Southeast Asia, and Africa). A similar divergence in the carbon sink between Tropical and Boreal forest was recognized by Tagesson et al.26.In terms of carbon stock change, our estimates are substantially higher than those reported by Pan et al.7 for 1990–2007 (+ 153 Tg C yr-1) based on FIP data. The biomass carbon estimates by Liu et al.6 are instead in line with our results. There is an increase in the annual rate of AGB in Russia of + 329 Tg C yr−1 (annual variation from 214 to 400 Tg C yr−1) over 2000–20076. Interestingly, another boreal country – Canada – has demonstrated neutral or negative trends (from 0 to -14 Tg C yr−1) for the same time span using the same estimation method6.We can observe different spatial patterns in the change in the GSV density between 1988 (FIP10, bias corrected11) and 2014 (our estimate), which can be explained by climate change, CO2 fertilisation and changes in disturbance regimes (Fig. 2). The average linear trend in the annual temperature increase during 1976–2014 in Russia is + 0.45 °C per 10 years27. The temperature increase is statistically significant in every region except for western Siberia (Fig. 2–3). Significantly increased temperature extremes and an increase in the number of days without precipitation is observed in the south of European Russia, Baikal, Kamchatka, and Chukotka27 (Fig. 2–1). Some regions in the south of the European part of Russia are colored in dark blue, but they, as a rule, have a small share of forested area, which is often linked to water bodies and, therefore, suffers less from increased drought (Fig. 2–1). Central and eastern Siberia suffer from an increase in disturbances, which offsets the climate stimulation effect (Fig. 2–4). The forested area in the Nenets region (Fig. 2–2) is 4 times larger in 2014 based on the RS forest mask compared to the SFR in 1988 (where forest was accounted for up until a certain latitude at that time), where the increase in area resulted in a decrease in the average GSV.Figure 2Change in growing stock volume (m3 ha-1) from 1988 to 2014 (average over administrative regions) (Generated by Esri ArcGIS Desktop v.10.7, URL: https://desktop.arcgis.com/en/arcmap/). These changes can be categorized into: 1—significant increase in air temperature and drought; 2—substantially increased forest area, which lowers the average GSV density; 3—least (not significant) temperature increase; 4—increase of disturbances: wildfire and harvest (southern part), which offsets the climate stimulation effect.Full size imageFocusing specifically on national reporting of managed forest to the UNFCCC, 72% of forested area in Russia is considered to be managed1. We multiplied the GSV density by the managed forest area for each administrative region (Table S3). The difference in GSV estimation (between ours and the one from the SFR report) is 23.6 × 109 m3 (Table S3) or 33% higher. From the GSV of managed forests in 2014 and based on the same area in 1988, we can estimate the sequestration rate of live biomass of managed forests as 354 Tg C yr-1 , which is considerably higher than the figure of 230 Tg C yr-1 in the current report1.This proof of concept demonstrates the relevance of complementing recent NFI data with remote sensing map products. Our study demonstrates that the already considerable value of forest inventory data can be further enhanced in a forest resources mapping scenario. In addition, we seek to promote greater access to these data by opening up their access to the larger scientific community. Through the integration of RS estimates of GSV and forest inventory data from Russia, we confirm that carbon stocks increased substantially during the last few decades in contrast to the figures provided in official national reporting. Russian forests play an even more important global role in carbon sequestration than previously thought, where the increase in growing stock is of the same magnitude as the net losses in tropical forests over the same time period. More

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A uniform response of the pollen grains towards storage conditions was registered in all five shrubs investigated with a conspicuous decline in germination percentage and pollen tube length after storage. Pollen tube growth reacted more sensitively to storage than germination. The most profound reductions in pollen viability traits were observed in samples stored at + 4 °C. The germination percentage of freshly collected pollen of individual shrubs ranged between 67.3 and 88.6%, whereas that in stored pollen was between 18.0 and 39.6%. In relative terms, storage represented a 49.3–73.2% decline in germination (Fig. 1). The same tendency was also observed in pollen tube growth, when freshly collected pollen possessed 248.0–367.3 µm long pollen tubes, and pollen stored at + 4 °C was characterised by 93.9–218.5 µm long pollen tubes. The corresponding decline reached 32.5–68.7%.Figure 1Graphical illustrations of variation in pollen germination percentage (a) and pollen tube length (b) of individual shrubs revealed in fresh pollen and in pollen under storage. Different letters refer to the statistical significance of the differences between tested individuals and storage variants, resulting from Duncan’s pairwise tests.Full size imageContrary to storage at + 4 °C, pollen stored at − 20 °C had an increased germination by 0.3% in shrub no. 1 and 0.6% in shrub no. 5 as compared with fresh pollen. A more conspicuous increase in pollen germinability was registered in individual no. 4, exhibiting 70.0% germination in fresh pollen and 93.6% in pollen stored at − 20 °C. In the remaining two shrubs (no. 2, 3), only a negligible decline in pollen germination was recorded. The deviation from freshly collected pollen varied within 0.5–16.8%. In general, the germination characteristics of pollen stored at − 20 °C were comparable with those of the fresh pollen and varied between 67.6 and 93.6%. As a second viability trait, pollen tube growth deviated more profoundly from that of fresh pollen than germination. On average, the pollen tube length of pollen stored at − 20 °C ranged from 163.0 to 286.6 µm, which represents a 11.4–45.7% decline compared to fresh pollen (Figs. 1, S1). ANOVA and Duncan`s grouping confirmed the highly significant differences between tested shrubs in both pollen germination percentage (P  More

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The experiment was conducted at Agricultural and Bio-Systems Engineering Department, Faculty of Agriculture Moshtohor, Benha University, Egypt (latitude 30° 21′ N and 31° 13′ E), during the period of May to July, 2019 season under the university guidelines and legislation. Basil seedlings were sown in the plastic cups (7 cm diameter and 7 cm height) filled with peat moss. The cups were irrigated daily using water with nutrient solution (Ca(NO3)2, 236 g L−1, KNO3, 101 g L−1, K2SO4, 115 g L−1, KH2PO4, 136 g L−1, MgSO4 246 g L−1 and chelates for trace elements into preacidified groundwater (from the following ppm concentration are achieved in this formulation: N = 210, P = 31, K = 234, Ca = 200, Mg = 48, S = 64, Fe = 14, Mn = 0.5, Zn = 0.05, Cu = 0.02, B = 0.5, Mo = 0.01)). Two weeks old basil seedlings were planted at 9.0 plant m−2 in the experimental tanks. These seedlings were planted according to the permission of Benha university rules and legislation.Culture systems descriptionFigure 1a,b show the experimental setup. It shows the system which consists of hydroponic system, aeroponic system, soilless substrate, solution system and pumps.Figure 1(a) The experimental setup. (b) Images of system.Full size imageThe hydroponic system (Deep Water Culture (DWC)) consists of three rectangular polyethylene tanks that used for basil plants culture. Dimensions of each tank are 80 cm long, 40 cm wide and 30 cm high. The slope of hydroponic tanks was 2% and stand 1 m high above the ground. The hydroponic tanks were covered with foam boards to support the plants. Each hydroponic tank provided with an air blower (Model NS 780—Flow Rate 850 L h−1—Head 1.5 m—Power 15 W, China) to increase dissolved oxygen concentrations. The solution was circulated by a pump (Model First QB60—Flow Rate 30 L min−1—Head 25 m—Power 0.5 hp, China) from the solution tank to the upper ends of the hydroponic tanks. Small tubes (16 mm) were used to provide tanks with solution in a closed system.Aeroponic system consists of three rectangular polyethylene tanks that used for basil plants culture. Dimensions of each tank are 80 cm long, 40 cm wide and 50 cm high. The aeroponic tanks were established 1 m above the ground. Each aeroponic tank was divided into two parts, the lower part was made from polyethylene and the upper part was made from wood. The aeroponic tanks were covered with foam boards to support the plants. Each aeroponic tank was provided with two fog nozzles (Model M3MNWT5M – Orifice 2 mm – Discharge 8 L h−1, India) located at the bottom of the tank sprayed nutrient solution into the tank in order to keep the roots wet. Small tubes (16 mm) were used to provide aeroponic tank with solution in a closed system.Soilless substrates consist are placed in three rows are 2 m long. Each row consists standard peat moss slabs (1.00 m × 0.20 m × 0.075 m). Basil plants were placed on row peat moss slabs with a drip irrigation system. There were three plants per slab giving a mean density of 9.0 plant m−2. Each plant was fed by a single drip.The circular polyethylene tank of the nutrient solution system 500 L capacity was used for collecting the drained solution by gravity from the ends of the three systems. The nutrient solutions were prepared manually once per ten days17,18 by dissolving appropriate amounts of Ca(NO3)2, 236 g L−1, KNO3, 101 g L−1, K2SO4, 115 g L−1, KH2PO4, 136 g L−1, MgSO4 246 g L−1 and chelates for trace elements into preacidified groundwater (from the following ppm concentration are achieved in this formulation: N = 210, P = 31, K = 234, Ca = 200, Mg = 48, S = 64, Fe = 14, Mn = 0.5, Zn = 0.05, Cu = 0.02, B = 0.5, Mo = 0.01). pH and Electrical Conductivity (EC) were further adjusted to 6.5–7.0 and 1.4–1.8 dS m−1, respectively, after salt addition. The average air ambient temperature was 25.97 ± 4.37 °C and the average water temperature was 24.03 ± 3.92 °C. The average relative humidity was 65.4% and the light intensity was 338.55 ± 40.06 W m−2.MeasurementsThree plants sample were taken during the vegetative and flowering stages (four and seven weeks after transplanting, respectively) for growth measurement and chemical analysis. Plant height, root length and the fresh and dry weight of leaves, stems and roots were determined. After measuring fresh mass, the plants were oven dried at 65 °C until constant weight was reached19. Total content of macro elements was evaluated after being digested20. Nitrogen was determined by Kjeldahl digestion methods21. Potassium, Calcium and magnesium were determined by Photofatometer (Model Jenway PFP7—Range 0—160 mmol L−1, USA) and phosphorus (P) was determined colorimetrically method22. The content of oil was determined in different organs: leaves, stems and inflorescences according to23.Water samples were taken, at inlet and outlet of the culture units for measuring nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg) were measured every week at 10 am during the experimental period.Total production costThe cost calculation based on the following parameters was also performed:Fixed costs (Fc)Depreciation costs (Dc)$$D_{c} = frac{{P_{d} – S_{r} }}{{L_{d} }}$$
(1)

where Dc is the depreciation cost, EGP (Egyptian pound) year−1. ($ = 15.63 EGP). Pd is the system price, EGP. Sr is the salvage rate (0.1Pd) EGP. Ld is the system life, year.Interest costs (In):$$I_{n} = frac{{P_{d} + S_{r} }}{2} times {text{i}}_{{text{n}}}$$
(2)

where In is the interest, EGP year−1. in is the interest as compounded annually, decimal (12%). Shelter, taxes and insurance costs (Si).Shelter, taxes and insurance costs were assumed to be 3% of the purchase price of the automatic feeder (Pm).Then:$${text{Fixed,cost }} = {text{ D}}_{{text{c}}} + {text{ I}}_{{text{n}}} + 0.03{text{ P}}_{{text{m}}} /{text{ hour, of, use ,per ,year}}$$
(3)
Variable (operating) costs (Vc)Repair and maintenance costs (Rm):$${text{R}}_{{text{m}}} = 100% ;{text{deprecation,cost/hour,of,use,per,year}}$$
(4)
Energy costs (E):$${text{E }} = {text{ EC }} times {text{ EP}}$$
(5)

where E is the energy costs, EGP h−1. EC is the electrical energy consumption, kWh. EP is the energy price, 0.57 EGP kW−1.Labor costs (La):$${text{L}}_{{text{a}}} = {text{ Salary, of, one, worker }} times {text{ No}}{text{. ,of, workers}}$$
(6)

where La is the Labor costs, EGP h−1. Salary of one worker = 10 EGP h−1. No. of workers = 1.Then:$${text{Variable,costs }} = {text{ Rm }} + {text{ E }} + {text{ La}}$$
(7)
Total costs (Tc)$${text{Total ,costs }} = {text{ Fixed ,costs }} + {text{ Variable ,costs}}$$
(8)

Table 1 shows the input parameters of calculate total production costs of basil plants grown in different soilless systems.Table 1 The input parameters of calculate total production costs of basil plants grown in different soilless systems.Full size tableNutrients consumption rateThe Nutrients consumption rate were calculated as the differences between the nutrients at inlet and outlet of culture units by the following formula24:$$C_{{Nc}} = frac{{Nc_{{in}} – Nc_{{out}} }}{{{text{Number, of ,plants}}}} times Q times {text{24}}$$
(9)

where CNc is the nutrients consumption rate, mg day−1 plant −1. Ncin is the nutrients at inlet of the hydroponic unit, mg L−1. Ncout is the nutrients at outlet of the hydroponic unit, mg L−1. Q is the discharge, L h−1.Model development of nutrient consumptionModel assumptions:

N, P, K, Ca and Mg are the nutrients used in study.

The plants are uniformity distributed in the solution, so they work as a uniform sink for water and minerals with space at any time.

The root systems are uniformly dispersed in the solution with uniform root length density at any time.

The whole root system uptake characteristics are uniform.

Water losses by evaporation are negligible.

The simplest nutrient consumption models relate the nutrient consumption to the concentration gradient using some sort of proportionality factor such as root permeability or conductivity25,26. The nutrient consumption was determined by using the following equation:$$NC = a_{{NC}} cdot Delta {text{C }}$$
(10)

where NC is the nutrient consumption, mg plant−1 day−1. ∆C is the concentration gradient, mg plant−1 day−1. aNC is the proportionality factor, dimensionless.A similar model of nutrient consumption takes into consideration the differing effects caused by variations in root growth stage. Assuming that growth follows a first order differential equation and assuming that the root growth is exponential27, then Eq. (11) can be derived. This equation is presented in similar form to Eq. (10) and use the following equation:$$NC = left( {frac{{left( {C_{{plant}} – {text{C}}_{{{text{plant0}}}} } right)}}{{A_{r} – A_{{r0}} }}} right) cdot left( {frac{{{text{ln}}left( {frac{{{text{A}}_{{text{r}}} }}{{{text{A}}_{{{text{r0}}}} }}} right)}}{{{text{t}} – {text{t}}_{0} }}} right){text{.A}}_{{text{r}}}$$
(11)

where Cplanto is the concentration of the nutrients in the plant at time t0, mg plant−1. Ar is the root surface area at time t, cm2 plant−1. Ar0 is the root surface area at time t0, cm2 plant−1.Root surface area was calculated from root length and mean root radius using the following equation:$$A_{r} = {text{2}}pi {text{r}}_{{text{0}}} {text{L}}_{{text{r}}}$$
(12)
The root length increment using the following equation28:$$Delta L_{r} = Delta DW_{{root}} {text{v }}$$
(13)

where ∆Lr is the root length increment, cm day−1. ∆DWroot is the daily amount of root dry mass increment, g day−1. v is the ratio of root length and mass of roots, cm g−1.The daily amount of dry weight of roots is calculated from the following equation29:$$Delta DW_{{root}} = left{ {begin{array}{*{20}l} {{text{5LAI}}} hfill & {{text{for,LAI}} le {{0}}{{.5}}} hfill \ {{{2}}{{.5}} + {{23}}{{.9}}left( {{text{LAI-0}}{{.5}}} right)} hfill & {{text{for,LAI}} > {{0}}{{.5}}} hfill \ end{array} } right.$$
(14)

where LAI is the leaf area index.Leaf area index was changed in the same proportions as root length density to maintain a constant ratio between roots and shoots. The leaf area index is calculated from the following equation30:$$LAI = frac{{LAI_{{max }} }}{{1 + K_{2} e^{{left( { – k_{1} t} right)}} }}$$
(15)

where LAImax is the maximum leaf area index. K2 and k1 are the coefficients of the growth functions.All computational procedures of the model were carried out using Excel spreadsheet. The computer program was devoted to mass balance for predicting the nutrients consumption. The differences between the predicted and measured values were evaluated using RMSE indicator (root means square error) which is calculated using the following equation:$$RMSE = sqrt {frac{{sum {left( {Predicted-Measured} right)^{2} } }}{n}}$$
(16)
The parameters used in the model that were obtained from the literature are listed in Table 2. Figure 2 shows flow chart of the model.Table 2 The parameters used in the model.Full size tableFigure 2Flow chart of nutrients consumption rate.Full size imageStatistical analysisThree replicates of each treatment were allocated in a Randomize Complete Block Design (RCBD) in the system. Data were analyzed one-way ANOVA (analysis of variance) using statistical package for social sciences (spss v21). Means were separated using New Duncan Multiple Range Test (DMRT). Data presented are mean ± standard division (SD) of four replicates. More

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